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The Influence of the Presence of Borax and NaCl on Water Absorption Pattern during Sturgeon Caviar (Acipenser transmontanus) Storage

The Influence of the Presence of Borax and NaCl on Water Absorption Pattern during Sturgeon... sensors Letter The Influence of the Presence of Borax and NaCl on Water Absorption Pattern during Sturgeon Caviar (Acipenser transmontanus) Storage 1 2 2 1 Massimo Brambilla , Marina Buccheri , Maurizio Grassi , Annamaria Stellari , 3 1 2 , Mario Pazzaglia , Elio Romano and Tiziana M. P. Cattaneo * Consiglio per la Ricerca in Agricoltura e L’analisi Dell’economia Agraria–CREA–Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via Milano, 43–24047 Treviglio (Bergamo), Italy; [email protected] (M.B.); [email protected] (A.S.); [email protected] (E.R.) Consiglio per la Ricerca in Agricoltura e L’analisi Dell’economia Agraria–CREA–Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via G. Venezian, 26–20133 Milano, Italy; [email protected] (M.B.); [email protected] (M.G.) Agroittica Lombarda S.p.A., Via Kennedy, 25012 Calvisano Brescia, Italy; [email protected] * Correspondence: [email protected]; Tel.: +39-02-239-557-212 Received: 10 November 2020; Accepted: 10 December 2020; Published: 15 December 2020 Abstract: Sturgeon caviar quality relies not only on the perfect dosage of the ingredients but also on the long sturgeon breeding cycle (about 12–15 years) and the exact timing of the egg extraction. For the improvement and the promotion of Italian caviar, the development of an analytical system dedicated to fish products, and caviar, in particular, is fundamental. The use of near-infrared spectrometry (NIRS) technology is auspicious. The aquaphotomics approach proved to be an adequate analytical tool to highlight, in real-time, the di erences in caviar quality stored with, or without, borax as a preservative. Seventy-five sturgeon caviar (Acipenser transmontanus) samples underwent spectral NIR characterization using a microNIR1700 in the 900–1700 nm range. Data processing was carried out according to the literature. Tenderometric and sensory analyses were also carried out in parallel. The results suggest that a process line under strict control and monitoring can result in high-quality caviar without any other preservative than salt. The challenge of producing caviar without any potentially-toxic preservatives could now be a reality. NIR spectroscopy and aquaphotomics can be, in the future, non-invasive methods to monitor the whole production chain. Keywords: NIR spectroscopy; aquaphotomics; caviar quality; portable instrumentation; sustainability; qualitative determination; food chain monitoring 1. Introduction Sturgeon caviar (from now on, called caviar) is a food product whose preparation appears extremely simple, requiring sturgeon eggs and salt. However, its quality relies not only on the perfect dosage of the ingredients, but also on: (i) the long sturgeon breeding cycle (about 12–15 years), and (ii) the exact timing of the egg extraction. Various techniques establish how to process sturgeon roe to caviar, as [1] reported. These processing techniques significantly impact product composition and quality and, thereby, marketability [2–5]. The preparation processes di er mainly on the salt content (NaCl), ranging from 3.2% to almost 11.8% among the producers. The high salt content results in dehydration, which increases the concentration of lipids and proteins in a linear pattern. Nowadays, sturgeon breeding in Lombardy (Italy) is mainly oriented to caviar production and has attained global manufacturing leadership in this field. The Lombard caviar production exceeds 40 tons/year, 90% of which represents the export for a value of about 15,000,000 euros. The appearance Sensors 2020, 20, 7174; doi:10.3390/s20247174 www.mdpi.com/journal/sensors Sensors 2020, 20, 7174 2 of 7 on the international market of products from countries with high production capacities and lower costs could represent a new obstacle for Lombard businesses and livestock. Not being able to compete, in terms of price, it is necessary to continue investing in product quality and innovation. Given the high costs of the raw materials, the caviar industry must o er an end product with the following features: (i) stable quality; (ii) sensory attributes well characterized; and (iii) adequate documental support from reliable and low-cost methods of investigation. Salting is considered the more delicate phase in light of the high variability of the roe. Compared to the wild one, the farming environment reduces the eggs’ variability; however, the inclusion in the regional context of new species of sturgeon and new farming conditions makes it dicult to calibrate the correct salting mixture for each circumstance. Italian business experiences have shown that di erent production batches, subjected to the same dosage, could result in di erent salt contents of the final product. During the product’s maturation, variable loss of brine occurs at varying of the salt mixture. For the improvement and the promotion of Italian caviar, the development of an analytical system dedicated to fish products and caviar is fundamental, and the use of near-infrared spectrometry (NIRS) technology is auspicious, not only for the quality of the final product, but also as an innovative control tool along the production line [6,7]. The European Union and the national legislations still allow some boron compounds (i.e., E284: boric acid-E285: sodium tetraborate) as preserving agents in caviar; however, recently, the Codex Alimentarius and some national regulations have pushed towards a borax-free product. Although the EFSA (European Food Safety Authority), in its latest scientific opinion of 2013 [8], confirmed the limited use of borax for caviar preparation, the international market prefers a borax-free product. Many countries have already prohibited the use of borax. The realization of a product free of preservatives improves its safety and its end quality. Among the di erent NIR technologies and procedures, several papers have reported the use of aquaphotomics as an immediate indicator of biological systems changes through the study of water pattern modification in the 1300–1500 nm NIR region [9]. This work aims to study the e ect of borax on caviar storage, to develop a borax-free product with suitable organoleptic characteristics throughout the shelf life. The aquaphotomics approach was chosen as an adequate analytical tool to identify borax as a preservative in caviar during the curing process. A rapid and objective method that allows the discrimination of the two treatments (salt+borax and borax-free) is a prerequisite to improve caviar ’s quality and safety at the end of the production chain. Fast detection and measurement of the borax presence would be intrinsically part of an innovation process of total quality control. 2. Materials and Methods Materials: Spectra from seventy-five caviar (Acipenser transmontanus) samples were collected from Agroittica Lombarda S.p.A. (Calvisano, Brescia, IT), an Italian caviar factory known at the international level. Spectra were collected at scheduled times: before treatment (“no salt” samples) and after treatment, with 3.5% NaCl or a mixture of 3.5% NaCl + 0.04% sodium tetraborate at the time 0 and after 90, 150, and 210 days of storage. On the same samples, the tenderometric analysis was carried out using a Texture Analyzer TA 32 XT PLUS II (Stable Micro Systems Ltd., Godalming, Surrey GU7 1YL, UK), modified to test individual eggs at the constant temperature of 0 C. The values of egg consistency and the distance and time the instrument took from the beginning of the measurement to the instant of the egg breakage were measured on every single egg (30 eggs/sample). NIR measurements took place directly on caviar samples before the packaging in jars (50 g each, time 0), and afterwards, at the opening of the jars during storage. Visual inspection detected the potential presence of molds, and a panel of 20 expert panelists carried out the sensory analysis on the caviar samples at the end of the storage period at the factory headquarter. Spectroscopy and Chemometrics: A portable MicroNIR 1700 spectrometer (VIAVI Solutions Italia SRL, Monza, Italy) was used for the spectra collection in the range of 900 to 1700 nm (200 scans; 128 points; three replicates; reflectance mode, resolution: 6.0 nm; signal/noise ratio—S/N: 25,000). The spectral data, converted in absorbance, were preprocessed according to [10] to verify the suitability Sensors 2020, 20, x FOR PEER REVIEW 3 of 8 spectral data, converted in absorbance, were preprocessed according to [10] to verify the suitability Sensors 2020, 20, 7174 3 of 7 of the holistic aquaphotomics approach in highlighting the differences arising between borax and no borax samples during the storage. Excel spreadsheet (Office 365, Microsoft Corporation, Redmond, of the holistic aquaphotomics approach in highlighting the di erences arising between borax and no WA, USA) and MINITAB 17.0 statistical software (Minitab Inc., State College, PA, USA) were used borax samples during the storage. Excel spreadsheet (Oce 365, Microsoft Corporation, Redmond, for data processing. Repeatability between replicates was verified. Aquagrams were built up at time WA, USA) and MINITAB 17.0 statistical software (Minitab Inc., State College, PA, USA) were used 0 and during storage (at 90, 150, and 210 days after the jar packaging), both for “borax” and “no for data processing. Repeatability between replicates was verified. Aquagrams were built up at time borax” sets of samples. Principal component analysis (PCA) was applied (95% of confidence level) to 0 and during storage (at 90, 150, and 210 days after the jar packaging), both for “borax” and “no the whole dataset, selecting the wavelength regions from 1300 to 1550 nm. Outlier detection in the borax” sets of samples. Principal component analysis (PCA) was applied (95% of confidence level) to multivariate space was carried out using the Mahalanobis distance criteria: observations falling above the whole dataset, selecting the wavelength regions from 1300 to 1550 nm. Outlier detection in the the critical distance were labeled as outliers and removed. The principal component analysis allowed multivariate space was carried out using the Mahalanobis distance criteria: observations falling above the extraction of useful information from the dataset, the exploration of its structure, and the global the critical distance were labeled as outliers and removed. The principal component analysis allowed correlation of the variables. Subsequently, using the most uncorrelated frequencies (i.e., 1342, 1374, the extraction of useful information from the dataset, the exploration of its structure, and the global and 1426 nm), the dataset underwent linear discriminant analysis (LDA) [11], one of the most used correlation of the variables. Subsequently, using the most uncorrelated frequencies (i.e., 1342, 1374, classification procedures, to assess the discrimination power of the aquaphotomics approach between and 1426 nm), the dataset underwent linear discriminant analysis (LDA) [11], one of the most used borax and no borax samples. The dataset underwent splitting into two datasets: a calibration set, classification procedures, to assess the discrimination power of the aquaphotomics approach between containing 2/3 of the observations chosen randomly, and a validation set, made up of the borax and no borax samples. The dataset underwent splitting into two datasets: a calibration set, remaining 1/3. containing 2/3 of the observations chosen randomly, and a validation set, made up of the remaining 1/3. 3. Results and Discussion 3. Results and Discussion Some examples of the quality of the collected spectra of sturgeon caviar samples are shown in Some examples of the quality of the collected spectra of sturgeon caviar samples are shown in Figure 1. Figure 1. Figure 1. Example of caviar spectra. Each color represents a NIR acquisition (sample). Figure 1. Example of caviar spectra. Each color represents a NIR acquisition (sample). The ranges of the variability of the main constituents of the analyzed samples are reported in The ranges of the variability of the main constituents of the analyzed samples are reported in Table 1. It is essential to consider that the composition can vary by individual constituent by about Table 1. It is essential to consider that the composition can vary by individual constituent by about five percentage points, within the same species, except for the ash content. These data were already five percentage points, within the same species, except for the ash content. These data were already presented during the 6th Italian Symposium NIRItalia 2014 [12], as preliminary results. presented during the 6th Italian Symposium NIRItalia 2014 [12], as preliminary results. Table 1. Concentration ranges of the main constituents of the matrix [12]. Table 1. Concentration ranges of the main constituents of the matrix [12]. Constituent (%) Min Max Average SD Constituent (%) min max average 52.10 58.53 55.46 SD Moisture 1.43 Protein 22.48 27.06 24.75 1.38 Moisture 52.10 58.53 55.46 1.43 Fat 13.64 18.46 16.41 1.32 Protein 22.48 27.06 24.75 1.38 Ash 1.31 4.20 3.47 0.71 Fat 13.64 18.46 16.41 1.32 Ash 1.31 4.20 3.47 0.71 The aquaphotomics approach was applied for the first time in the present work as a holistic approach to point out whether borax’s addition a ects the caviar storage and the quality of the final The aquaphotomics approach was applied for the first time in the present work as a holistic product. Figure 2 reports the repeatability of measurements; the aquagrams of the samples before and approach to point out whether borax’s addition affects the caviar storage and the quality of the final after salt (NaCl) addition are shown (three replicates each). Sensors 2020, 20, x FOR PEER REVIEW 4 of 8 Sensors 2020, 20, x FOR PEER REVIEW 4 of 8 product. Figure 2 reports the repeatability of measurements; the aquagrams of the samples before Sensors 2020, 20, 7174 4 of 7 product. Figure 2 reports the repeatability of measurements; the aquagrams of the samples before and after salt (NaCl) addition are shown (three replicates each). and after salt (NaCl) addition are shown (three replicates each). Figure 2. Aquagrams of samples before (red lines), and after (blue lines), salt (NaCl) addition—three Figure 2. Aquagrams of samples before (red lines), and after (blue lines), salt (NaCl) addition—three replicates each. replicates each. Figure 2. Aquagrams of samples before (red lines), and after (blue lines), salt (NaCl) addition—three replicates each. Differences highlighted on the aquagrams between salted and no salted samples are due to salt’s Di erences highlighted on the aquagrams between salted and no salted samples are due to salt’s hydration power, mainly resulting in an increase of absorbance in the NIR range from 1400 to 1500 hydration power, mainly resulting in an increase of absorbance in the NIR range from 1400 to 1500 nm. Differences highlighted on the aquagrams between salted and no salted samples are due to salt’s nm. Results suggested continuing the study of the influence of preservatives (NaCl, borax) by using Results suggested continuing the study of the influence of preservatives (NaCl, borax) by using the hydration power, mainly resulting in an increase of absorbance in the NIR range from 1400 to 1500 the aquaphotomics approach, due to the perfect fitting of absorbance curves among replicates aquaphotomics approach, due to the perfect fitting of absorbance curves among replicates (Figure 2). nm. Results suggested continuing the study of the influence of preservatives (NaCl, borax) by using (Figure 2). Figure 3 reports the aquagrams of the borax and no borax samples. the aquaphotomics approach, due to the perfect fitting of absorbance curves among replicates Figure 3 reports the aquagrams of the borax and no borax samples. (Figure 2). Figure 3 reports the aquagrams of the borax and no borax samples. Figure 3. Caviar aquagrams during ripening in borax (on the left) and no borax (on the right) samples. Figure 3. Caviar aquagrams during ripening in borax (on the left) and no borax (on the right) samples. The red lines (bright and dark) refer to acquisitions at 0 and 90 days; the green lines represent samples The red lines (bright and dark) refer to acquisitions at 0 and 90 days; the green lines represent samples at 150 days, and the blue lines the samples at 210 days from packaging. at 150 days, and the blue lines the samples at 210 days from packaging. Figure 3. Caviar aquagrams during ripening in borax (on the left) and no borax (on the right) samples. In Figure 3, the aquagrams built up during caviar storage are reported. The absorbance of samples The red lines (bright and dark) refer to acquisitions at 0 and 90 days; the green lines represent samples In Figure 3, the aquagrams built up during caviar storage are reported. The absorbance of containing salt (NaCl) or salt + borax showed similar profiles along the storage time in the spectral at 150 days, and the blue lines the samples at 210 days from packaging. samples containing salt (NaCl) or salt + borax showed similar profiles along the storage time in the range from 1340 to 1512 nm. Significant di erences in absorbance values were only noted at 210 days spectral range from 1340 to 1512 nm. Significant differences in absorbance values were only noted at In Figure 3, the aquagrams built up during caviar storage are reported. The absorbance of of storage for both sets of samples, suggesting that the use of borax does not introduce significant 210 days of storage for both sets of samples, suggesting that the use of borax does not introduce anomalies samples cont that ain may ing s result alt (N in aCl quality ) or sa deterioration lt + borax showe during d sithe mila pr r pro eservation files along of caviar the st . orage time in the significant anomalies that may result in quality deterioration during the preservation of caviar. spectral range from 1340 to 1512 nm. Significant differences in absorbance values were only noted at Applying the PCA to the whole dataset pointed out the possibility to discriminate between Applying the PCA to the whole dataset pointed out the possibility to discriminate between samples 210 days of storage for bo treated with salt (NaCl) th sets and of samples, sugge those treated with sting th salt +at the borax, use o as shown f borax in does not Figure 4. introduce samples treated with salt (NaCl) and those treated with salt + borax, as shown in Figure 4. significant anomalies that may result in quality deterioration during the preservation of caviar. Applying the PCA to the whole dataset pointed out the possibility to discriminate between samples treated with salt (NaCl) and those treated with salt + borax, as shown in Figure 4. Sensors 2020, 20, x FOR PEER REVIEW 5 of 8 Sensors 2020, 20, 7174 5 of 7 Sensors 2020, 20, x FOR PEER REVIEW 5 of 8 Figure 4. Principal component analysis (PCA) applied to the whole caviar sample set in the aquagram range: NaCl salt (square), borax (circle), a = short time of storage; b = long time of storage; c = whole borax set. Figure 4. Figure 4. Princip Pr ainci l copal component analysis ( mponent analysis (PCA P) CA) app applied to lied to th e the whole whole ca caviar sa viar sam mple ple set in the set in theaquagra aquagr m am range: The score plot, constructed using PC1 vs. PC2 (98.9% of explained variance), showed that, along range: NaCl salt (square), borax (circle), a = short time of storage; b = long time of storage; c = whole NaC the PC1 l salt (sq (8 u3 a.r 1% of exp e), borax (lc ai ir ned va cle), a ria = sn hce or — t t95% ime of confide of storage;nb ce level), no = long time borax of sto r and age b ; co= rax w samples were hole borax set . borax set. discriminated with positive score values associated with the borax set. In comparison, the PC2 (15.8% The of exp scor lained e plot, variance constr ) see ucted med to using split t PC1 he observ vs. atio PC2 ns (98.9% into two of groups, dependi explained variance), ng on the length of showed that, The score plot, constructed using PC1 vs. PC2 (98.9% of explained variance), showed that, along storage, with lower score values associated with longer conservation time. along the PC1 (83.1% of explained variance—95% of confidence level), no borax and borax samples the PC1 (83.1% of explained variance—95% of confidence level), no borax and borax samples were Figure 5 shows the primary wavelengths responsible for PCA results. were discriminated with positive score values associated with the borax set. In comparison, the PC2 discriminated with positive score values associated with the borax set. In comparison, the PC2 (15.8% st The PCA applied to the whole dataset enabled identifying borax’s presence on the 1 principal (15.8% of explained variance) seemed to split the observations into two groups, depending on the of explained variance) seemed to split the observations into two groups, depending on the length of component. Based on the absorbance differences in the 1400–1430 nm wavelengths range, such a storage, with lower score values associated with longer conservation time. length of storage, with lower score values associated with longer conservation time. result is ascribable to the first overtone bands of the OH stretching vibration of water molecules. This Figure 5 shows the primary wavelengths responsible for PCA results. Figure 5 shows the primary wavelengths responsible for PCA results. finding follows NaCl and borax’s different hydration capacities, resulting from their chemical st The PCA applied to the whole dataset enabled identifying borax’s presence on the 1 principal structures that differ in their potential interactions with water molecules. component. Based on the absorbance differences in the 1400–1430 nm wavelengths range, such a result is ascribable to the first overtone bands of the OH stretching vibration of water molecules. This 0.2 finding follows NaCl and borax’s different hydration capacities, resulting from their chemical 0.1 1460 1440 structures that differ in their potential interactions with water molecules. 14 88 0.0 -0.1 -0.2 0.2 1 14 452 12 -0.3 0.1 1460 1440 -0.4 1488 0.0 -0.5 -0.1 1426 -0.6 -0.2 -0.7 -0.3 -0.8 -0.4 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 -0.5 First Compon en t -0.6 -0.7 -0.8 Figure 5. PCA loading plot. Figure 5. PCA loading plot. -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 First Compon en t The LDA carried out on the calibration set (43 observations) resulted in the following linear The PCA applied to the whole dataset enabled identifying borax’s presence on the 1st discrimination functions: principal component. Based on the absorbance di erences in the 1400–1430 nm wavelengths range, Figure 5. PCA loading plot. such a result is ascribable to the first overtone bands of the OH stretching vibration of water(1 molecules. ) Borax 15.71 879.19 ∙ “1342 nm” – 724.82 ∙ “1374 nm” 991.4 ∙ “142 6 nm” This finding The LDA follows cNaCl arried out and on t borax’s he calidi brat er ion set ent hydration (43 observat capacities, ions) resulted resulting in the fofr llow om ing thei line r ar chemic al No Borax 2.51 205.9 ∙ “1342 nm” – 95.23 ∙ “1374 nm” 258.56 ∙ “1 426 nm” (2) discrimination functions: structures that di er in their potential interactions with water molecules. Overall, the classification procedure achieved a good classification rate (97.7%) (Table 2): the best The LDA carried out on the calibration set (43 observations) resulted in the following linear (1) Borax 15.71 879.19 ∙ “1342 nm” – 724.82 ∙ “1374 nm” 991.4 ∙ “142 6 nm” classification performance regarded the observations from no borax samples, while 1 out of 20 borax discrimination functions: samples resulted in the wrong classification. No Borax 2.51 205.9 ∙ “1342 nm” – 95.23 ∙ “1374 nm” 258.56 ∙ “1 426 nm” (2) Borax = 15.71 + 879.19“1342 nm” – 724.82“1374 nm” + 991.4“1426 nm” (1) Overall, the classification procedure achieved a good classification rate (97.7%) (Table 2): the best classification performance regarded the observations from no borax samples, while 1 out of 20 borax samples resulted in the wrong classification. No Borax = 2.51 + 205.9“1342 nm” – 95.23“1374 nm” + 258.56“1426 nm” (2) Overall, the classification procedure achieved a good classification rate (97.7%) (Table 2): the best classification performance regarded the observations from no borax samples, while 1 out of 20 borax samples resulted in the wrong classification. Secon d Component Secon d Component Sensors 2020, 20, 7174 6 of 7 Table 2. Classification matrix of the calibration set. Borax No Borax Sensors 2020, 20, x FOR PEER REVIEW 6 of 8 Borax 19 0 Table 2. Classification matrix of the calibration set. No borax 1 23 Total 20 23 Borax No borax Correctly classified 19 23 Borax 19 0 No borax 1 23 Total 20 23 The application of the discriminant equations (Equations (1) and (2)) to the validation set resulted Correctly classified 19 23 in the classification matrix of Table 3: The application of the discriminant equations (Equations (1) and (2)) to the validation set resulted in the classification matrix of Table 3: Table 3. Classification matrix of the validation set. Table 3. Classification matrix of the validation set. Borax No Borax Borax Borax 10 No borax 0 No borax 0 12 Borax 10 0 Total 10 12 No borax 0 12 Correctly classified 10 12 Total 10 12 Correctly classified 10 12 Such high classification rates for both borax and no borax samples support the adequacy of the Such high classification rates for both borax and no borax samples support the adequacy of the aquaphotomic approach in monitoring the caviar ’s ripening and detecting the presence of borax. aquaphotomic approach in monitoring the caviar’s ripening and detecting the presence of borax. Figure 6 sh Fig ow ure s 6 thsho e te w n s the dero tender metri ometric d c data ex ata pr expressed essed (fr o (fr m om le le ftft t t oor ri ig gh ht t)) as as (i( )i va ) v lu al es u of es to hf e max the m imum aximum peak peak force; (ii) the time required for the egg breaking; and (iii) the distance the plate traveled to break force; (ii) the time required for the egg breaking; and (iii) the distance the plate traveled to break the eggs. the eggs. Figure 6. Results of the tenderometric analysis. Figure 6. Results of the tenderometric analysis. The analysis of the two groups of samples (borax and no borax) did not point out significant The analysis of the two groups of samples (borax and no borax) did not point out significant differences for the consistency of the eggs (measured in terms of the maximum peak force the di erences for the consistency of the eggs (measured in terms of the maximum peak force the instrument instrument applies to achieve eggs breakage). applies to achieve eggs breakage). However, time and distance values to achieve the egg breakage were lower in the borax samples, suggesting a lower elasticity of the eggs receiving borax in the brining process than those treated with However, time and distance values to achieve the egg breakage were lower in the borax samples, NaCl only. The difference in the dehydration capacity of added salts could be one reason for such suggesting a lower elasticity of the eggs receiving borax in the brining process than those treated with different elasticity, resulting from differences in water clusters’ formation. If confirmed, this finding NaCl only. The di erence in the dehydration capacity of added salts could be one reason for such could affect both the shelf life duration and the end quality of the caviar. The panel test also pointed di erent elasticity, resulting from di erences in water clusters’ formation. If confirmed, this finding out such lower elasticity of the eggs, relating it with lower organoleptic quality of borax samples (data not shown). The preliminary results obtained by texture analyzer and sensory analysis suggests a could a ect both the shelf life duration and the end quality of the caviar. The panel test also pointed lower quality of the caviar produced using sodium tetraborate as a preservative, supporting the need out such lower elasticity of the eggs, relating it with lower organoleptic quality of borax samples to have a rapid method to identify the sodium tetraborate presence in caviar batches. (data not shown). The preliminary results obtained by texture analyzer and sensory analysis suggests a lower quality of the caviar produced using sodium tetraborate as a preservative, supporting the need 4. Conclusions to have a rapid method to identify the sodium tetraborate presence in caviar batches. The aquaphotomics approach was shown to be adequate in studying the storage process of caviar. Based on the external perturbation induced by the two preservatives on the water response, 4. Conclusions it was possible to distinguish between borax and no borax samples using a portable NIR instrument when a high S/N value is assured. The LDA applied to the validation set achieved high classification The aquaphotomics approach was shown to be adequate in studying the storage process of caviar. rates for both borax and no borax samples. Differences in chemical structure between the two types Based on the of salt used external allowed the perturbation detection of bor induced ax, d by ue to the its two different hyd preservatives ration power on the , even if water add response ed in a , it was small percentage. possible to distinguish between borax and no borax samples using a portable NIR instrument when a high S/N value is assured. The LDA applied to the validation set achieved high classification rates for both borax and no borax samples. Di erences in chemical structure between the two types of salt used allowed the detection of borax, due to its di erent hydration power, even if added in a small percentage. NIR spectroscopy and aquaphotomics can be, in the future, used as non-invasive methods to discriminate between fish origin, as suggested by preliminary results reported in the technical report Sensors 2020, 20, 7174 7 of 7 of the project n. 201300004629, funded by Lombardy Region (data not published) and to potentially monitor the whole production chain. Author Contributions: Conceptualization: T.M.C. and M.B. (Marina Buccheri); methodology: M.B. (Marina Buccheri); software: E.R.; validation: M.B. (Massimo Brambilla), A.S., and M.G.; formal analysis: A.S. and M.G.; investigation: M.B. (Marina Buccheri); resources: M.P.; data curation: M.B. (Massimo Brambilla); writing—original draft preparation: T.M.C.; writing—review and editing: M.B. (Marina Buccheri); visualization: T.M.C.; supervision: M.B. (Massimo Brambilla); project administration: M.P.; funding acquisition: M.P. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Lombardy Region (Italy), Measure 124–PSR 2007–2013”. Project n. 201300004629. Acknowledgments: Authors thank Agroittica Lombarda S.p.A. [Calvisano (Brescia) Italy] for samples collection and technical support. The study was partially funded by Lombardy Region (Italy). Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. References 1. Gessner, J.; Wirth, M.; Kirschbaum, F.; Patriche, N. Processing techniques for caviar and their e ect on product composition. Int. Rev. Hydrobiol. 2002, 86, 645–650. [CrossRef] 2. Wirth, M.; Kirschbaum, F.; Gessner, J.; Krüger, A.; Patriche, N.; Billard, R. Chemical and biochemical composition of caviar from di erent sturgeon species and origins. Food/Nahrung 2000, 44, 233–237. [CrossRef] 3. 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Servid, S.A.; Talbott, M.J.; Van Eenennaam, J.P.; Doroshov, S.I.; Struffenegger, P.; Webb, M.A.H.; Cavinato, A.G. Rapid non-invasive characterization of ovarian follicular atresia in cultured white sturgeon (Acipenser transmontanus) by near infrared spectroscopy. Aquaculture 2011, 315, 290–297. [CrossRef] 8. Aguilar, F.; Crebelli, R.; Dusemund, B.; Galtier, P.; Gott, D.; Gundert-Remy, U.; König, J.; Lambré, C.; Leblanc, J.-C.-; Mosesso, P.; et al. Scientific Opinion on the re-evaluation of boric acid (E 284) and sodium tetraborate (borax) (E 285) as food additives. EFSA J. 2013, 11, 3407. 9. Muncan, J.; Tsenkova, R. Aquaphotomics—From Innovative Knowledge to Integrative Platform in Science and Technology. Molecules 2019, 24, 2742. [CrossRef] [PubMed] 10. Tsenkova, R.; Muncan, J.; Pollner, B.; Kovacs, Z. Essentials of Aquaphotomics and Its Chemometrics Approaches. Front. Chem. 2018, 6. [CrossRef] [PubMed] 11. Todeschini, R. Introduzione alla Chemiometria; EdiSES: Naples, Italy, 1998; Volume 6.5, pp. 104–105. ISBN 8879591460. 12. Grassi, M.; Barzaghi, S.; Buccheri, M.; Pazzaglia, M.; Vasconi, M.; Cattaneo, T.M.P. La spettroscopia NIR applicata al controllo qualità del caviale: Risultati preliminari (NIR spectroscopy applied to caviar quality control: Preliminary results). In Proceedings of the Atti VI Simposio Italiano di Spettroscopia NIR, Alla Giusta Frequenza, Modena, Italy, 28–30 May 2014; pp. 140–145, ISBN 9788890406485. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional aliations. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Sensors Multidisciplinary Digital Publishing Institute

The Influence of the Presence of Borax and NaCl on Water Absorption Pattern during Sturgeon Caviar (Acipenser transmontanus) Storage

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sensors Letter The Influence of the Presence of Borax and NaCl on Water Absorption Pattern during Sturgeon Caviar (Acipenser transmontanus) Storage 1 2 2 1 Massimo Brambilla , Marina Buccheri , Maurizio Grassi , Annamaria Stellari , 3 1 2 , Mario Pazzaglia , Elio Romano and Tiziana M. P. Cattaneo * Consiglio per la Ricerca in Agricoltura e L’analisi Dell’economia Agraria–CREA–Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via Milano, 43–24047 Treviglio (Bergamo), Italy; [email protected] (M.B.); [email protected] (A.S.); [email protected] (E.R.) Consiglio per la Ricerca in Agricoltura e L’analisi Dell’economia Agraria–CREA–Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via G. Venezian, 26–20133 Milano, Italy; [email protected] (M.B.); [email protected] (M.G.) Agroittica Lombarda S.p.A., Via Kennedy, 25012 Calvisano Brescia, Italy; [email protected] * Correspondence: [email protected]; Tel.: +39-02-239-557-212 Received: 10 November 2020; Accepted: 10 December 2020; Published: 15 December 2020 Abstract: Sturgeon caviar quality relies not only on the perfect dosage of the ingredients but also on the long sturgeon breeding cycle (about 12–15 years) and the exact timing of the egg extraction. For the improvement and the promotion of Italian caviar, the development of an analytical system dedicated to fish products, and caviar, in particular, is fundamental. The use of near-infrared spectrometry (NIRS) technology is auspicious. The aquaphotomics approach proved to be an adequate analytical tool to highlight, in real-time, the di erences in caviar quality stored with, or without, borax as a preservative. Seventy-five sturgeon caviar (Acipenser transmontanus) samples underwent spectral NIR characterization using a microNIR1700 in the 900–1700 nm range. Data processing was carried out according to the literature. Tenderometric and sensory analyses were also carried out in parallel. The results suggest that a process line under strict control and monitoring can result in high-quality caviar without any other preservative than salt. The challenge of producing caviar without any potentially-toxic preservatives could now be a reality. NIR spectroscopy and aquaphotomics can be, in the future, non-invasive methods to monitor the whole production chain. Keywords: NIR spectroscopy; aquaphotomics; caviar quality; portable instrumentation; sustainability; qualitative determination; food chain monitoring 1. Introduction Sturgeon caviar (from now on, called caviar) is a food product whose preparation appears extremely simple, requiring sturgeon eggs and salt. However, its quality relies not only on the perfect dosage of the ingredients, but also on: (i) the long sturgeon breeding cycle (about 12–15 years), and (ii) the exact timing of the egg extraction. Various techniques establish how to process sturgeon roe to caviar, as [1] reported. These processing techniques significantly impact product composition and quality and, thereby, marketability [2–5]. The preparation processes di er mainly on the salt content (NaCl), ranging from 3.2% to almost 11.8% among the producers. The high salt content results in dehydration, which increases the concentration of lipids and proteins in a linear pattern. Nowadays, sturgeon breeding in Lombardy (Italy) is mainly oriented to caviar production and has attained global manufacturing leadership in this field. The Lombard caviar production exceeds 40 tons/year, 90% of which represents the export for a value of about 15,000,000 euros. The appearance Sensors 2020, 20, 7174; doi:10.3390/s20247174 www.mdpi.com/journal/sensors Sensors 2020, 20, 7174 2 of 7 on the international market of products from countries with high production capacities and lower costs could represent a new obstacle for Lombard businesses and livestock. Not being able to compete, in terms of price, it is necessary to continue investing in product quality and innovation. Given the high costs of the raw materials, the caviar industry must o er an end product with the following features: (i) stable quality; (ii) sensory attributes well characterized; and (iii) adequate documental support from reliable and low-cost methods of investigation. Salting is considered the more delicate phase in light of the high variability of the roe. Compared to the wild one, the farming environment reduces the eggs’ variability; however, the inclusion in the regional context of new species of sturgeon and new farming conditions makes it dicult to calibrate the correct salting mixture for each circumstance. Italian business experiences have shown that di erent production batches, subjected to the same dosage, could result in di erent salt contents of the final product. During the product’s maturation, variable loss of brine occurs at varying of the salt mixture. For the improvement and the promotion of Italian caviar, the development of an analytical system dedicated to fish products and caviar is fundamental, and the use of near-infrared spectrometry (NIRS) technology is auspicious, not only for the quality of the final product, but also as an innovative control tool along the production line [6,7]. The European Union and the national legislations still allow some boron compounds (i.e., E284: boric acid-E285: sodium tetraborate) as preserving agents in caviar; however, recently, the Codex Alimentarius and some national regulations have pushed towards a borax-free product. Although the EFSA (European Food Safety Authority), in its latest scientific opinion of 2013 [8], confirmed the limited use of borax for caviar preparation, the international market prefers a borax-free product. Many countries have already prohibited the use of borax. The realization of a product free of preservatives improves its safety and its end quality. Among the di erent NIR technologies and procedures, several papers have reported the use of aquaphotomics as an immediate indicator of biological systems changes through the study of water pattern modification in the 1300–1500 nm NIR region [9]. This work aims to study the e ect of borax on caviar storage, to develop a borax-free product with suitable organoleptic characteristics throughout the shelf life. The aquaphotomics approach was chosen as an adequate analytical tool to identify borax as a preservative in caviar during the curing process. A rapid and objective method that allows the discrimination of the two treatments (salt+borax and borax-free) is a prerequisite to improve caviar ’s quality and safety at the end of the production chain. Fast detection and measurement of the borax presence would be intrinsically part of an innovation process of total quality control. 2. Materials and Methods Materials: Spectra from seventy-five caviar (Acipenser transmontanus) samples were collected from Agroittica Lombarda S.p.A. (Calvisano, Brescia, IT), an Italian caviar factory known at the international level. Spectra were collected at scheduled times: before treatment (“no salt” samples) and after treatment, with 3.5% NaCl or a mixture of 3.5% NaCl + 0.04% sodium tetraborate at the time 0 and after 90, 150, and 210 days of storage. On the same samples, the tenderometric analysis was carried out using a Texture Analyzer TA 32 XT PLUS II (Stable Micro Systems Ltd., Godalming, Surrey GU7 1YL, UK), modified to test individual eggs at the constant temperature of 0 C. The values of egg consistency and the distance and time the instrument took from the beginning of the measurement to the instant of the egg breakage were measured on every single egg (30 eggs/sample). NIR measurements took place directly on caviar samples before the packaging in jars (50 g each, time 0), and afterwards, at the opening of the jars during storage. Visual inspection detected the potential presence of molds, and a panel of 20 expert panelists carried out the sensory analysis on the caviar samples at the end of the storage period at the factory headquarter. Spectroscopy and Chemometrics: A portable MicroNIR 1700 spectrometer (VIAVI Solutions Italia SRL, Monza, Italy) was used for the spectra collection in the range of 900 to 1700 nm (200 scans; 128 points; three replicates; reflectance mode, resolution: 6.0 nm; signal/noise ratio—S/N: 25,000). The spectral data, converted in absorbance, were preprocessed according to [10] to verify the suitability Sensors 2020, 20, x FOR PEER REVIEW 3 of 8 spectral data, converted in absorbance, were preprocessed according to [10] to verify the suitability Sensors 2020, 20, 7174 3 of 7 of the holistic aquaphotomics approach in highlighting the differences arising between borax and no borax samples during the storage. Excel spreadsheet (Office 365, Microsoft Corporation, Redmond, of the holistic aquaphotomics approach in highlighting the di erences arising between borax and no WA, USA) and MINITAB 17.0 statistical software (Minitab Inc., State College, PA, USA) were used borax samples during the storage. Excel spreadsheet (Oce 365, Microsoft Corporation, Redmond, for data processing. Repeatability between replicates was verified. Aquagrams were built up at time WA, USA) and MINITAB 17.0 statistical software (Minitab Inc., State College, PA, USA) were used 0 and during storage (at 90, 150, and 210 days after the jar packaging), both for “borax” and “no for data processing. Repeatability between replicates was verified. Aquagrams were built up at time borax” sets of samples. Principal component analysis (PCA) was applied (95% of confidence level) to 0 and during storage (at 90, 150, and 210 days after the jar packaging), both for “borax” and “no the whole dataset, selecting the wavelength regions from 1300 to 1550 nm. Outlier detection in the borax” sets of samples. Principal component analysis (PCA) was applied (95% of confidence level) to multivariate space was carried out using the Mahalanobis distance criteria: observations falling above the whole dataset, selecting the wavelength regions from 1300 to 1550 nm. Outlier detection in the the critical distance were labeled as outliers and removed. The principal component analysis allowed multivariate space was carried out using the Mahalanobis distance criteria: observations falling above the extraction of useful information from the dataset, the exploration of its structure, and the global the critical distance were labeled as outliers and removed. The principal component analysis allowed correlation of the variables. Subsequently, using the most uncorrelated frequencies (i.e., 1342, 1374, the extraction of useful information from the dataset, the exploration of its structure, and the global and 1426 nm), the dataset underwent linear discriminant analysis (LDA) [11], one of the most used correlation of the variables. Subsequently, using the most uncorrelated frequencies (i.e., 1342, 1374, classification procedures, to assess the discrimination power of the aquaphotomics approach between and 1426 nm), the dataset underwent linear discriminant analysis (LDA) [11], one of the most used borax and no borax samples. The dataset underwent splitting into two datasets: a calibration set, classification procedures, to assess the discrimination power of the aquaphotomics approach between containing 2/3 of the observations chosen randomly, and a validation set, made up of the borax and no borax samples. The dataset underwent splitting into two datasets: a calibration set, remaining 1/3. containing 2/3 of the observations chosen randomly, and a validation set, made up of the remaining 1/3. 3. Results and Discussion 3. Results and Discussion Some examples of the quality of the collected spectra of sturgeon caviar samples are shown in Some examples of the quality of the collected spectra of sturgeon caviar samples are shown in Figure 1. Figure 1. Figure 1. Example of caviar spectra. Each color represents a NIR acquisition (sample). Figure 1. Example of caviar spectra. Each color represents a NIR acquisition (sample). The ranges of the variability of the main constituents of the analyzed samples are reported in The ranges of the variability of the main constituents of the analyzed samples are reported in Table 1. It is essential to consider that the composition can vary by individual constituent by about Table 1. It is essential to consider that the composition can vary by individual constituent by about five percentage points, within the same species, except for the ash content. These data were already five percentage points, within the same species, except for the ash content. These data were already presented during the 6th Italian Symposium NIRItalia 2014 [12], as preliminary results. presented during the 6th Italian Symposium NIRItalia 2014 [12], as preliminary results. Table 1. Concentration ranges of the main constituents of the matrix [12]. Table 1. Concentration ranges of the main constituents of the matrix [12]. Constituent (%) Min Max Average SD Constituent (%) min max average 52.10 58.53 55.46 SD Moisture 1.43 Protein 22.48 27.06 24.75 1.38 Moisture 52.10 58.53 55.46 1.43 Fat 13.64 18.46 16.41 1.32 Protein 22.48 27.06 24.75 1.38 Ash 1.31 4.20 3.47 0.71 Fat 13.64 18.46 16.41 1.32 Ash 1.31 4.20 3.47 0.71 The aquaphotomics approach was applied for the first time in the present work as a holistic approach to point out whether borax’s addition a ects the caviar storage and the quality of the final The aquaphotomics approach was applied for the first time in the present work as a holistic product. Figure 2 reports the repeatability of measurements; the aquagrams of the samples before and approach to point out whether borax’s addition affects the caviar storage and the quality of the final after salt (NaCl) addition are shown (three replicates each). Sensors 2020, 20, x FOR PEER REVIEW 4 of 8 Sensors 2020, 20, x FOR PEER REVIEW 4 of 8 product. Figure 2 reports the repeatability of measurements; the aquagrams of the samples before Sensors 2020, 20, 7174 4 of 7 product. Figure 2 reports the repeatability of measurements; the aquagrams of the samples before and after salt (NaCl) addition are shown (three replicates each). and after salt (NaCl) addition are shown (three replicates each). Figure 2. Aquagrams of samples before (red lines), and after (blue lines), salt (NaCl) addition—three Figure 2. Aquagrams of samples before (red lines), and after (blue lines), salt (NaCl) addition—three replicates each. replicates each. Figure 2. Aquagrams of samples before (red lines), and after (blue lines), salt (NaCl) addition—three replicates each. Differences highlighted on the aquagrams between salted and no salted samples are due to salt’s Di erences highlighted on the aquagrams between salted and no salted samples are due to salt’s hydration power, mainly resulting in an increase of absorbance in the NIR range from 1400 to 1500 hydration power, mainly resulting in an increase of absorbance in the NIR range from 1400 to 1500 nm. Differences highlighted on the aquagrams between salted and no salted samples are due to salt’s nm. Results suggested continuing the study of the influence of preservatives (NaCl, borax) by using Results suggested continuing the study of the influence of preservatives (NaCl, borax) by using the hydration power, mainly resulting in an increase of absorbance in the NIR range from 1400 to 1500 the aquaphotomics approach, due to the perfect fitting of absorbance curves among replicates aquaphotomics approach, due to the perfect fitting of absorbance curves among replicates (Figure 2). nm. Results suggested continuing the study of the influence of preservatives (NaCl, borax) by using (Figure 2). Figure 3 reports the aquagrams of the borax and no borax samples. the aquaphotomics approach, due to the perfect fitting of absorbance curves among replicates Figure 3 reports the aquagrams of the borax and no borax samples. (Figure 2). Figure 3 reports the aquagrams of the borax and no borax samples. Figure 3. Caviar aquagrams during ripening in borax (on the left) and no borax (on the right) samples. Figure 3. Caviar aquagrams during ripening in borax (on the left) and no borax (on the right) samples. The red lines (bright and dark) refer to acquisitions at 0 and 90 days; the green lines represent samples The red lines (bright and dark) refer to acquisitions at 0 and 90 days; the green lines represent samples at 150 days, and the blue lines the samples at 210 days from packaging. at 150 days, and the blue lines the samples at 210 days from packaging. Figure 3. Caviar aquagrams during ripening in borax (on the left) and no borax (on the right) samples. In Figure 3, the aquagrams built up during caviar storage are reported. The absorbance of samples The red lines (bright and dark) refer to acquisitions at 0 and 90 days; the green lines represent samples In Figure 3, the aquagrams built up during caviar storage are reported. The absorbance of containing salt (NaCl) or salt + borax showed similar profiles along the storage time in the spectral at 150 days, and the blue lines the samples at 210 days from packaging. samples containing salt (NaCl) or salt + borax showed similar profiles along the storage time in the range from 1340 to 1512 nm. Significant di erences in absorbance values were only noted at 210 days spectral range from 1340 to 1512 nm. Significant differences in absorbance values were only noted at In Figure 3, the aquagrams built up during caviar storage are reported. The absorbance of of storage for both sets of samples, suggesting that the use of borax does not introduce significant 210 days of storage for both sets of samples, suggesting that the use of borax does not introduce anomalies samples cont that ain may ing s result alt (N in aCl quality ) or sa deterioration lt + borax showe during d sithe mila pr r pro eservation files along of caviar the st . orage time in the significant anomalies that may result in quality deterioration during the preservation of caviar. spectral range from 1340 to 1512 nm. Significant differences in absorbance values were only noted at Applying the PCA to the whole dataset pointed out the possibility to discriminate between Applying the PCA to the whole dataset pointed out the possibility to discriminate between samples 210 days of storage for bo treated with salt (NaCl) th sets and of samples, sugge those treated with sting th salt +at the borax, use o as shown f borax in does not Figure 4. introduce samples treated with salt (NaCl) and those treated with salt + borax, as shown in Figure 4. significant anomalies that may result in quality deterioration during the preservation of caviar. Applying the PCA to the whole dataset pointed out the possibility to discriminate between samples treated with salt (NaCl) and those treated with salt + borax, as shown in Figure 4. Sensors 2020, 20, x FOR PEER REVIEW 5 of 8 Sensors 2020, 20, 7174 5 of 7 Sensors 2020, 20, x FOR PEER REVIEW 5 of 8 Figure 4. Principal component analysis (PCA) applied to the whole caviar sample set in the aquagram range: NaCl salt (square), borax (circle), a = short time of storage; b = long time of storage; c = whole borax set. Figure 4. Figure 4. Princip Pr ainci l copal component analysis ( mponent analysis (PCA P) CA) app applied to lied to th e the whole whole ca caviar sa viar sam mple ple set in the set in theaquagra aquagr m am range: The score plot, constructed using PC1 vs. PC2 (98.9% of explained variance), showed that, along range: NaCl salt (square), borax (circle), a = short time of storage; b = long time of storage; c = whole NaC the PC1 l salt (sq (8 u3 a.r 1% of exp e), borax (lc ai ir ned va cle), a ria = sn hce or — t t95% ime of confide of storage;nb ce level), no = long time borax of sto r and age b ; co= rax w samples were hole borax set . borax set. discriminated with positive score values associated with the borax set. In comparison, the PC2 (15.8% The of exp scor lained e plot, variance constr ) see ucted med to using split t PC1 he observ vs. atio PC2 ns (98.9% into two of groups, dependi explained variance), ng on the length of showed that, The score plot, constructed using PC1 vs. PC2 (98.9% of explained variance), showed that, along storage, with lower score values associated with longer conservation time. along the PC1 (83.1% of explained variance—95% of confidence level), no borax and borax samples the PC1 (83.1% of explained variance—95% of confidence level), no borax and borax samples were Figure 5 shows the primary wavelengths responsible for PCA results. were discriminated with positive score values associated with the borax set. In comparison, the PC2 discriminated with positive score values associated with the borax set. In comparison, the PC2 (15.8% st The PCA applied to the whole dataset enabled identifying borax’s presence on the 1 principal (15.8% of explained variance) seemed to split the observations into two groups, depending on the of explained variance) seemed to split the observations into two groups, depending on the length of component. Based on the absorbance differences in the 1400–1430 nm wavelengths range, such a storage, with lower score values associated with longer conservation time. length of storage, with lower score values associated with longer conservation time. result is ascribable to the first overtone bands of the OH stretching vibration of water molecules. This Figure 5 shows the primary wavelengths responsible for PCA results. Figure 5 shows the primary wavelengths responsible for PCA results. finding follows NaCl and borax’s different hydration capacities, resulting from their chemical st The PCA applied to the whole dataset enabled identifying borax’s presence on the 1 principal structures that differ in their potential interactions with water molecules. component. Based on the absorbance differences in the 1400–1430 nm wavelengths range, such a result is ascribable to the first overtone bands of the OH stretching vibration of water molecules. This 0.2 finding follows NaCl and borax’s different hydration capacities, resulting from their chemical 0.1 1460 1440 structures that differ in their potential interactions with water molecules. 14 88 0.0 -0.1 -0.2 0.2 1 14 452 12 -0.3 0.1 1460 1440 -0.4 1488 0.0 -0.5 -0.1 1426 -0.6 -0.2 -0.7 -0.3 -0.8 -0.4 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 -0.5 First Compon en t -0.6 -0.7 -0.8 Figure 5. PCA loading plot. Figure 5. PCA loading plot. -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 First Compon en t The LDA carried out on the calibration set (43 observations) resulted in the following linear The PCA applied to the whole dataset enabled identifying borax’s presence on the 1st discrimination functions: principal component. Based on the absorbance di erences in the 1400–1430 nm wavelengths range, Figure 5. PCA loading plot. such a result is ascribable to the first overtone bands of the OH stretching vibration of water(1 molecules. ) Borax 15.71 879.19 ∙ “1342 nm” – 724.82 ∙ “1374 nm” 991.4 ∙ “142 6 nm” This finding The LDA follows cNaCl arried out and on t borax’s he calidi brat er ion set ent hydration (43 observat capacities, ions) resulted resulting in the fofr llow om ing thei line r ar chemic al No Borax 2.51 205.9 ∙ “1342 nm” – 95.23 ∙ “1374 nm” 258.56 ∙ “1 426 nm” (2) discrimination functions: structures that di er in their potential interactions with water molecules. Overall, the classification procedure achieved a good classification rate (97.7%) (Table 2): the best The LDA carried out on the calibration set (43 observations) resulted in the following linear (1) Borax 15.71 879.19 ∙ “1342 nm” – 724.82 ∙ “1374 nm” 991.4 ∙ “142 6 nm” classification performance regarded the observations from no borax samples, while 1 out of 20 borax discrimination functions: samples resulted in the wrong classification. No Borax 2.51 205.9 ∙ “1342 nm” – 95.23 ∙ “1374 nm” 258.56 ∙ “1 426 nm” (2) Borax = 15.71 + 879.19“1342 nm” – 724.82“1374 nm” + 991.4“1426 nm” (1) Overall, the classification procedure achieved a good classification rate (97.7%) (Table 2): the best classification performance regarded the observations from no borax samples, while 1 out of 20 borax samples resulted in the wrong classification. No Borax = 2.51 + 205.9“1342 nm” – 95.23“1374 nm” + 258.56“1426 nm” (2) Overall, the classification procedure achieved a good classification rate (97.7%) (Table 2): the best classification performance regarded the observations from no borax samples, while 1 out of 20 borax samples resulted in the wrong classification. Secon d Component Secon d Component Sensors 2020, 20, 7174 6 of 7 Table 2. Classification matrix of the calibration set. Borax No Borax Sensors 2020, 20, x FOR PEER REVIEW 6 of 8 Borax 19 0 Table 2. Classification matrix of the calibration set. No borax 1 23 Total 20 23 Borax No borax Correctly classified 19 23 Borax 19 0 No borax 1 23 Total 20 23 The application of the discriminant equations (Equations (1) and (2)) to the validation set resulted Correctly classified 19 23 in the classification matrix of Table 3: The application of the discriminant equations (Equations (1) and (2)) to the validation set resulted in the classification matrix of Table 3: Table 3. Classification matrix of the validation set. Table 3. Classification matrix of the validation set. Borax No Borax Borax Borax 10 No borax 0 No borax 0 12 Borax 10 0 Total 10 12 No borax 0 12 Correctly classified 10 12 Total 10 12 Correctly classified 10 12 Such high classification rates for both borax and no borax samples support the adequacy of the Such high classification rates for both borax and no borax samples support the adequacy of the aquaphotomic approach in monitoring the caviar ’s ripening and detecting the presence of borax. aquaphotomic approach in monitoring the caviar’s ripening and detecting the presence of borax. Figure 6 sh Fig ow ure s 6 thsho e te w n s the dero tender metri ometric d c data ex ata pr expressed essed (fr o (fr m om le le ftft t t oor ri ig gh ht t)) as as (i( )i va ) v lu al es u of es to hf e max the m imum aximum peak peak force; (ii) the time required for the egg breaking; and (iii) the distance the plate traveled to break force; (ii) the time required for the egg breaking; and (iii) the distance the plate traveled to break the eggs. the eggs. Figure 6. Results of the tenderometric analysis. Figure 6. Results of the tenderometric analysis. The analysis of the two groups of samples (borax and no borax) did not point out significant The analysis of the two groups of samples (borax and no borax) did not point out significant differences for the consistency of the eggs (measured in terms of the maximum peak force the di erences for the consistency of the eggs (measured in terms of the maximum peak force the instrument instrument applies to achieve eggs breakage). applies to achieve eggs breakage). However, time and distance values to achieve the egg breakage were lower in the borax samples, suggesting a lower elasticity of the eggs receiving borax in the brining process than those treated with However, time and distance values to achieve the egg breakage were lower in the borax samples, NaCl only. The difference in the dehydration capacity of added salts could be one reason for such suggesting a lower elasticity of the eggs receiving borax in the brining process than those treated with different elasticity, resulting from differences in water clusters’ formation. If confirmed, this finding NaCl only. The di erence in the dehydration capacity of added salts could be one reason for such could affect both the shelf life duration and the end quality of the caviar. The panel test also pointed di erent elasticity, resulting from di erences in water clusters’ formation. If confirmed, this finding out such lower elasticity of the eggs, relating it with lower organoleptic quality of borax samples (data not shown). The preliminary results obtained by texture analyzer and sensory analysis suggests a could a ect both the shelf life duration and the end quality of the caviar. The panel test also pointed lower quality of the caviar produced using sodium tetraborate as a preservative, supporting the need out such lower elasticity of the eggs, relating it with lower organoleptic quality of borax samples to have a rapid method to identify the sodium tetraborate presence in caviar batches. (data not shown). The preliminary results obtained by texture analyzer and sensory analysis suggests a lower quality of the caviar produced using sodium tetraborate as a preservative, supporting the need 4. Conclusions to have a rapid method to identify the sodium tetraborate presence in caviar batches. The aquaphotomics approach was shown to be adequate in studying the storage process of caviar. Based on the external perturbation induced by the two preservatives on the water response, 4. Conclusions it was possible to distinguish between borax and no borax samples using a portable NIR instrument when a high S/N value is assured. The LDA applied to the validation set achieved high classification The aquaphotomics approach was shown to be adequate in studying the storage process of caviar. rates for both borax and no borax samples. Differences in chemical structure between the two types Based on the of salt used external allowed the perturbation detection of bor induced ax, d by ue to the its two different hyd preservatives ration power on the , even if water add response ed in a , it was small percentage. possible to distinguish between borax and no borax samples using a portable NIR instrument when a high S/N value is assured. The LDA applied to the validation set achieved high classification rates for both borax and no borax samples. Di erences in chemical structure between the two types of salt used allowed the detection of borax, due to its di erent hydration power, even if added in a small percentage. NIR spectroscopy and aquaphotomics can be, in the future, used as non-invasive methods to discriminate between fish origin, as suggested by preliminary results reported in the technical report Sensors 2020, 20, 7174 7 of 7 of the project n. 201300004629, funded by Lombardy Region (data not published) and to potentially monitor the whole production chain. Author Contributions: Conceptualization: T.M.C. and M.B. (Marina Buccheri); methodology: M.B. (Marina Buccheri); software: E.R.; validation: M.B. (Massimo Brambilla), A.S., and M.G.; formal analysis: A.S. and M.G.; investigation: M.B. (Marina Buccheri); resources: M.P.; data curation: M.B. (Massimo Brambilla); writing—original draft preparation: T.M.C.; writing—review and editing: M.B. (Marina Buccheri); visualization: T.M.C.; supervision: M.B. (Massimo Brambilla); project administration: M.P.; funding acquisition: M.P. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Lombardy Region (Italy), Measure 124–PSR 2007–2013”. Project n. 201300004629. Acknowledgments: Authors thank Agroittica Lombarda S.p.A. [Calvisano (Brescia) Italy] for samples collection and technical support. The study was partially funded by Lombardy Region (Italy). Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. References 1. Gessner, J.; Wirth, M.; Kirschbaum, F.; Patriche, N. Processing techniques for caviar and their e ect on product composition. Int. Rev. Hydrobiol. 2002, 86, 645–650. [CrossRef] 2. Wirth, M.; Kirschbaum, F.; Gessner, J.; Krüger, A.; Patriche, N.; Billard, R. Chemical and biochemical composition of caviar from di erent sturgeon species and origins. Food/Nahrung 2000, 44, 233–237. [CrossRef] 3. Gessner, J.; Wirth, M.; Kirschbaum, F.; Krüger, A.; Patriche, N. Caviar composition in wild and cultured sturgeons—Impact of food sources on fatty acid composition and contaminant load. J. Appl. Ichthyol. 2002, 18, 665–672. [CrossRef] 4. Gessner, J.; Würtz, S.; Kirschbaum, F.; Wirth, M. Biochemical composition of caviar as a tool to discriminate between aquaculture and wild origin. J. Appl. Ichthyol. 2008, 24, 52–56. [CrossRef] 5. Monavar, H.M.; Alimardani, R.; Omid, M.; Woldand, J.P.; Razavi, H. Prediction of Fatty acid Contents of Caviar from Caspian Sea (Iran) using NIR Spectroscopy. In Proceedings of the International Conference on Agriculture, Chemical and Environmental Sciences (ICACES’2012), Dubai, UAE, 6–7 October 2012; pp. 118–121. 6. Cen, H.; He, Y. Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends Food Sci. Technol. 2007, 18, 72–83. [CrossRef] 7. 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Introduzione alla Chemiometria; EdiSES: Naples, Italy, 1998; Volume 6.5, pp. 104–105. ISBN 8879591460. 12. Grassi, M.; Barzaghi, S.; Buccheri, M.; Pazzaglia, M.; Vasconi, M.; Cattaneo, T.M.P. La spettroscopia NIR applicata al controllo qualità del caviale: Risultati preliminari (NIR spectroscopy applied to caviar quality control: Preliminary results). In Proceedings of the Atti VI Simposio Italiano di Spettroscopia NIR, Alla Giusta Frequenza, Modena, Italy, 28–30 May 2014; pp. 140–145, ISBN 9788890406485. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional aliations. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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Published: Dec 15, 2020

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