Nondestructive Determination of Moisture and Sodium Chloride in Cured Atlantic Salmon (Salmo salar) (Teijin) Using Short-wavelength Near-infrared Spectroscopy (SW-NIR)

ABSTRACT: Moisture (49.70 to 74.20% w/w) and salt (0.13 to 12.30% w/w) concentrations in cured Atlantic salmon ( Salmo salar ) or teijin were determined by short-wavelength near-infrared (SW-NIR) reflectance spectroscopy (600 to 1100 nm) using partial least square regression (PLS) and artificial neural networks (ANN) calibration methods. ANN and PLS yielded similar results (Salt: ANN RMS = 1.43% w/w, PLS RMS = 1.37% w/w; Water, ANN RMS = 2.08% w/w, PLS RMS = 2.04% w/w). Sampling the dorsal or ventral portion of the fish did not appear to affect the prediction error of the salt or moisture models.     

Multivariate determination of free fatty acids and moisture in fish oils by partial least-squares regression and near-infrared spectroscopy

The oxidative and hydrolytic degradation of lipids in fish oil was monitored using partial least-squares (PLS) regression and near-infrared reflectance (NIR) spectroscopy. One hundred and sixty (n=160) fish oil samples from a fishmeal factory were scanned in transflectance by an NIR monochromator instrument (1100–2500 nm). Calibration models were performed for free fatty acids (FFA), moisture (M), peroxide value (PV) and anisidine value (AV). Coefficients of determination in calibration (R²) and standard errors of cross validation (SECV) were 0.96 (SECV: 0.59) and 0.94 (SECV: 0.03) for FFA and M in g/kg, respectively. The accuracy of the NIR calibration models were tested using a validation set, yielding coefficients of correlation (r) and standard errors of prediction (SEP) of 0.98 (SEP: 0.50) and 0.80 (SEP: 0.05) for FFA and M in g/kg, respectively. Poor accuracy (R²<0.80) was obtained for the NIR calibration models developed for PV and AV. The paper demonstrates that fish oil hydrolytic degradation of lipids, which seriously affect oil use and storage under industrial conditions, can be successfully monitored using PLS regression and NIR spectroscopy by the fishmeal industry.     

Rapid assessment of the quality of deep frying oils used by street vendors with Fourier transform infrared spectroscopy

Deterioration of deep frying oils used by street vendors is one of the major food safety concerns. Fourier transform infrared (FTIR) spectroscopy coupled with partial least squares (PLS) regression was applied for rapid evaluation of the quality of deep frying oils collected from different street vendors (n = 109) using various frying processes in Shanghai. The levels of free fatty acids (FFA), total polar compounds (TPC), and viscosity of oils were determined with conventional methods and used as reference values for developing PLS models. The FFA (0.07–1.78 mg (KOH)/g) of all tested frying oils were below the maximum allowed value, while 5.5 % of oils had TPC (3.19–54.47 %) above the maximum allowed value (27 %) based upon the related chinese standards. FTIR coupled with PLS regression resulted in less satisfying results for FFA determination (predictability for soybean oils: R² = 0.709, SEP = 0.14, RPD = 1.83), but showed great promise for rapid determination of viscosity (model predictability: R² = 0.921–0.945, SEP = 0.68–0.71, RPD = 3.54–3.98) and TPC (predictability for soybean oils: R² = 0.790–0.931, SEP = 1.89–2.94, RPD = 2.16–3.55) of frying oils from different commercial settings. Developing separated PLS models for shortening and non-shortening oils improved predictability, particularly for the analysis of TPC.     

Rapid Near Infrared Spectroscopic Method for the Detection of Spoilage in Rainbow Trout (Oncorhynchus mykiss) Fillet

The possibility of using visible and short‐wavelength near‐infrared (SW‐NIR: 600 to 1100 nm) spectroscopy to detect the onset of spoilage and to quantify microbial loads in rainbow trout (Oncorhynchus mykiss) was investigated. Spectra were acquired on the skin and flesh side of intact trout fillet portions and on minced trout muscle samples stored at 4 °C for up to 8 d or at room temperature (21 °C) for 24 h. Principal component analysis (PCA) and partial least squares (PLS) chemometric models were developed to predict the onset and degree of spoilage. PCA results showed clear segregation between the control (day 1) and the samples held 4 d or longer at 4 °C. Clear segregation was observed for samples stored 10 h or longer at 21 °C compared with the control (0 h), indicating that onset of spoilage could be detected with this method. Quantitative PLS prediction models for microbial loads were established. For trout fillets, 4 °C: R= 0.97, standard error of prediction (SEP) = 0.38 log colony‐forming units (CFU)/g (flesh side); R= 0.94, SEP = 0.53 log CFU/g (skin side); R= 0.82, SEP = 0.82 log CFU/g for minced fish held at 21 °C. These results indicate that SW‐NIR in combination with multivariate statistical methods can be used to detect and monitor the spoilage process in rainbow trout and quantify microbial loads rapidly and accurately.     

Real-Time Monitoring of Organic Carrot (var. Romance) During Hot-Air Drying Using Near-Infrared Spectroscopy

The worldwide consumption of dried carrot (Daucus carota L.) is on a growing trend. Conventional methods for drying carrots include hot-water blanching followed by hot-air drying, which is usually uncontrolled and therefore prone to product quality deterioration. Thus, there is a need for innovative drying systems that yield high-value end products. In this study, the efficacy of NIR spectroscopy for the non-destructive monitoring of physicochemical changes and drying behaviour in organic carrot slices during 8-h hot-air drying at 40 °C was demonstrated using Partial least squares (PLS) regression and PLS discriminant analysis (PLS-DA). The impact of hot-water blanching pre-treatment (at 95 °C for 1.45 min) for enzyme inactivation on performances of both regression and classification models was also evaluated. PLS regression models were successfully developed to monitor changes in water activity (R ² = 0.91–0.96), moisture content (R ² = 0.97–0.98), total carotenoids content (R ² = 0.92–0.96), lightness for unblanched carrots (R ² = 0.80–0.83) and hue angle for blanched samples (R ² = 0.85–0.87). Soluble solids content prediction was poor for both treatments (RMSEP = 3.43–4.40). Classification models were developed to recognise dehydration phases of carrot slices on the basis of their NIR spectral profile using K-means and PLS-DA algorithms in sequence. The performance of each PLS-DA model was defined based on its accuracy, sensitivity and specificity rates. All of the selected models provided from good (> 0.85) to excellent (> 0.95) sensitivity and specificity for the predefined drying phases. Feature selection procedures yielded both regression and classification models with performances very similar to models computed from the full spectrum.     

Use of ATR-FTIR spectroscopy coupled with chemometrics for the authentication of avocado oil in ternary mixtures with sunflower and soybean oils

Avocado oil is a high-value and nutraceutical oil whose authentication is very important since the addition of low-cost oils could lower its beneficial properties. Mid-FTIR spectroscopy combined with chemometrics was used to detect and quantify adulteration of avocado oil with sunflower and soybean oils in a ternary mixture. Thirty-seven laboratory-prepared adulterated samples and 20 pure avocado oil samples were evaluated. The adulterated oil amount ranged from 2% to 50% (w/w) in avocado oil. A soft independent modelling class analogy (SIMCA) model was developed to discriminate between pure and adulterated samples. The model showed recognition and rejection rate of 100% and proper classification in external validation. A partial least square (PLS) algorithm was used to estimate the percentage of adulteration. The PLS model showed values of R² > 0.9961, standard errors of calibration (SEC) in the range of 0.3963–0.7881, standard errors of prediction (SEP estimated) between 0.6483 and 0.9707, and good prediction performances in external validation. The results showed that mid-FTIR spectroscopy could be an accurate and reliable technique for qualitative and quantitative analysis of avocado oil in ternary mixtures.     

Quality Evaluation of Shelled and Unshelled Macadamia Nuts by Means of Near‐Infrared Spectroscopy (NIR)

The quality of shelled and unshelled macadamia nuts was assessed by means of Fourier transformed near‐infrared (FT‐NIR) spectroscopy. Shelled macadamia nuts were sorted as sound nuts; nuts infected by Ecdytolopha aurantiana and Leucopteara coffeella; and cracked nuts caused by germination. Unshelled nuts were sorted as intact nuts (<10% half nuts, 2014); half nuts (March, 2013; November, 2013); and crushed nuts (2014). Peroxide value (PV) and acidity index (AI) were determined according to AOAC. PCA‐LDA shelled macadamia nuts classification resulted in 93.2% accurate classification. PLS PV prediction model resulted in a square error of prediction (SEP) of 3.45 meq/kg, and a prediction coefficient determination value (R_p²) of 0.72. The AI PLS prediction model was better (SEP = 0.14%, R_p² = 0.80). Although adequate classification was possible (93.2%), shelled nuts must not contain live insects, therefore the classification accuracy was not satisfactory. FT‐NIR spectroscopy can be successfully used to predict PV and AI in unshelled macadamia nuts, though.     

Simultaneous analysis of main catechins contents in green tea (Camellia sinensis (L.)) by Fourier transform near infrared reflectance (FT-NIR) spectroscopy

This paper reported the results of simultaneous analysis of main catechins (i.e., EGC, EC, EGCG and ECG) contents in green tea by the Fourier transform near infrared reflectance (FT-NIR) spectroscopy and the multivariate calibration. Partial least squares (PLS) algorithm was conducted on the calibration of regression model. The number of PLS factors and the spectral preprocessing methods were optimised simultaneously by cross-validation in the model calibration. The performance of the final model was evaluated according to root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficient (R). The correlations coefficients (R) in the prediction set were achieved as follows: R = 0.9852 for EGC model, R = 0.9596 for EC model, R = 0.9760 for EGCG model and R = 0.9763 for ECG model. This work demonstrated that NIR spectroscopy with PLS algorithm could be used to analyse main catechins contents in green tea.     

Rapid characterization and identification of fatty acids in margarines using horizontal attenuate total reflectance Fourier transform infrared spectroscopy (HATR-FTIR)

Fourier transform infrared spectroscopy (FTIR) with horizontal attenuated total reflectance (HATR) coupled to multivariate analysis was used to predict chemical composition, fatty acid profile, nutritional relationships between fatty acids, and to identify trans fatty acids (TFA) of margarines. For model building and validation, a set of 42 margarines samples were analyzed in terms of fatty acid profile, total fat, moisture, and salt content. The quantitative models were based on incorporating the above chemical parameters of the different margarines and HATR-FTIR spectral information into the calibration model using chemometric analysis. Chemical parameters for total fat, moisture, and salt content ranged 39–84.5%, 14.5–59%, and 0.3–2.6%, respectively. Regarding fatty acids, the concentration of TFA, saturated fatty acid (SFA), monounsaturated fatty acid (MUFA), and polyunsaturated fatty acid (PUFA) ranged 0–34.06%, 17.17–54.20%, 15.26–34.49%, and 4.02–53.89% (g/100 g margarine), respectively. Principal components regression (PCR) and partial least square analysis (PLS) were used to inspect the variation within the sample set. The best model to predict the chemical composition was obtained using the algorithm partial least squares (PLS) with a R ² greater than 0.933 and SEC and SEP less than 1.294 and 1.406, respectively. The optimized SIMCA model used to identify high or low TFA content showed 100% correct classification rate of both margarines with less than 2.0 g TFA/100 g fat as more than 2.0 g TFA/100 g fat. Compared with traditional chemical analysis, the FTIR-HATR models analyzed margarines in minutes and directly in their neat form.     

Monitoring water uptake in whole barley (Hordeum vulgare L.) grain during steeping using near infrared reflectance spectroscopy

The aim of this study was to evaluate the use of near infrared reflectance (NIR) spectroscopy to monitor water uptake and steeping time in whole barley samples as a rapid and easy to use technique. Whole barley grain samples were steeped in water, and subsamples were analyzed for water uptake (gravimetric method) and using NIR spectroscopy. The spectra and the analytical data were used to develop partial least squares (PLS) calibrations to predict water uptake and steeping time. Cross validation models for water uptake and steeping time gave a coefficient of determination in cross validation (R²) and the standard error of prediction (SEP) of 0.90 (SEP = 5.36 g/100 g fw) and 0.92 (SEP = 3.93 h), respectively. This study showed that NIR spectroscopy combined with PLS regression showed promise as a rapid, non-destructive method to monitor and measure water uptake and steeping time in whole barley during soaking.     

First Break Forward Interval PLS (FB-FiPLS) Procedure as Potential Tool in Analysis of FTIR Data for Fast and Robust Quantitative Determination of Food Adulteration

Ternary mixtures of sugar solutions containing maple syrup were studied quantitatively using Fourier transform infrared (FTIR) attenuated total reflectance (ATR) technique coupled with partial least squares regression (PLS) and selection of spectral variables. Two ternary mixtures were analyzed; first ternary mixture contained maple syrup, white sugar solution, and fully inverted sugar solution; second ternary mixture comprised maple syrup, white, and brown sugar solutions. In this paper, a procedure for selection of spectral variables with PLS, called first break forward interval PLS (FB-FiPLS), is tested on maple syrup adulteration. The method achieved almost exactly the same performance as synergy interval PLS (SiPLS) but with much shorter computational time. The upper limit of number of latent variables (LVs), which is the critical factor for both interval PLS methods, was determined using repeated double cross-validation on whole spectral region of calibration set for each analyzed component in each analyzed ternary mixture set. FB-FiPLS procedure for selection of spectral variables, using only root mean square error of cross validation (RMSECV) values for whole optimization of spectral variables, is fast and robust. After spectral variables and LVs for each particular model had been selected with minimum RMSECV of FB-FiPLS procedure, final results in terms of RMSECV and RMSEP for FB-FiPLS were in most cases statistically significantly better than PLS on whole spectral region and on selected spectral regions. Predictions of each component in analyzed ternary mixture set is promising (R ²(training set)?>?0.98, R ²(test set)?>?0.97), especially for fully inverted sugar solution (RMSEP?=?0.142 % w/w).     

Shortwave-near infrared spectroscopy for determination of reducing sugar content during grape ripening,winemaking, and aging of white and red wines

The feasibility of near infrared (NIR) spectroscopy for predicting reducing sugar content during grape ripening, winemaking, and aging was assessed. NIR calibration models were developed using a set of 146 samples scanned in a quartz flow cell with a 50 mm path length in the NIR region (800–1050 nm), in a fiber spectrometer system working in transmission mode. Principal component analysis (PCA), partial least squares (PLS), and multiple linear (MLR) regressions were used to interpret spectra and to develop calibrations for reducing sugar content in grape, must, and wine. The PLS model based on the full spectral range (800–1050 nm), yielded a determination coefficient (r²) of 0.98, a standard error of cross validation (SECV) of 13.62 g/l and a root mean square error of cross validation (RMSECV) of 13.58 g/l. The mathematical model was tested with independent validation samples (n = 48); the resulting values for r², the standard error of prediction (SEP) and the root mean square error of prediction (RMSEP) for the same parameter were 0.98, 10.84, and 12.20 g/l, respectively. The loading weights of latent variables from the PLS model were used to identify sensitive wavelengths. To assess their suitability, MLR models were built using these wavelengths. Wavelength significance was analyzed by ANOVA, and four wavelengths (909, 951, 961, and 975 nm) were selected, setting statistical significance at the 99% confidence level. The MLR model yielded acceptable results for r² (0.92), SEP (19.97 g/l) and RMSEP (20.51 g/l). The results suggest that NIR spectroscopy is a promising technique for predicting reducing sugar content during grape ripening, as well as during the fermentation and aging of white and red wines. Individual fingerprint wavelengths strongly associated with reducing sugar content could be used to enhance the efficacy of this simple, efficient and low-cost instrument.     

Use of sodium nitrite in salt-curing of Atlantic salmon (Salmo salar L.) – Impact on product quality

Quality changes during processing and quality differences in smoked fillets of Atlantic salmon (4–5 kg) salted with nitrite salt compared to table salted fillets were measured. Quality parameters from right-side fillets dry salted with nitrite salt were compared with the respective left-side fillets treated the same way with table salt. Ten raw right-side fillets were analysed and used as raw material reference. Use of nitrite salt in salt-curing of smoked salmon affected colour to a more reddish hue, tended to increase carotenoid stability and displayed positive effects on NaCl diffusivity. Only slight weight changes and change in texture properties were revealed. The use of nitrite salt displayed no adverse effects like increased content of N-nitrosoamines in smoked products. In fact, significant lower contents of N-nitrosoamines were found in nitrite salted smoked fillets compared to smoked fillets salted with table salt. Relatively high amounts of residual nitrite in nitrite-treated fillets seem to be the most prominent adverse effect caused by the use of nitrite salt in salt-curing of smoked Atlantic salmon.     

Potential use of physical measurements including ultrasound for a better mango fruit quality characterization

The potential use of ultrasound measurements, combined with other physical measurements, has been investigated. The good relationship between soluble solids content (SSC) and ultrasonic wave velocity reported in the literature being confirmed by our study, our main goal was to evaluate the added value of ultrasound measurements around 25 MHz for the determination of biochemical compounds responsible of organoleptic quality of mangoes. Among the main sugar constituents of mango juice, only sucrose content prediction was improved by combining SSC and ultrasonic waves velocity using a PLS model (R² = 0.81, RMSECV = 12.3, bias = 0.10, RPD = 2.3) when compared to the linear model with SSC only (R² = 0.75, SEP = 14.05, bias = 0.08). The same conclusion was obtained for titratable acidity PLS model using whole fruit hardness, SSC and ultrasonic wave velocity (R² = 0.82, RMSECV = 1.84, bias = 0.02, RPD = 2.4) compared to the linear model with fruit hardness only (R² = 0.78, SEP = 2.07, bias = 0.02). However, the added value of ultrasound measurements was not always found to be significant (P = 0.05) when a Wilcoxon statistical tests was conducted on the residuals of the linear and PLS models for both sucrose and titratable acidity.     

Effect of water phase salt content and storage temperature on Listeria monocytogenes survival in chum salmon (Oncorhynchus keta) roe and caviar (ikura)

ABSTRACT:  Salmon caviar, or ikura, is a ready-to-eat food prepared by curing the salmon roe in a brine solution. Other seasonings or flavorants may be added, depending upon the characteristics of the product desired. Listeria monocytogenes growth is a potential risk, since it can grow at high salt concentrations ( > 10%) and in some products at temperatures as low as 3 °C. Ikura was prepared from chum salmon ( Oncorhynchus keta ) roe by adding food-grade NaCl to yield water phase salt contents (WPS) of 0.22% (no added salt), 2.39%± 0.18%, 3.50%± 0.19%, and 4.36%± 0.36%. A cocktail containing L. monocytogenes (ATCC 19114, 7644, 19113) was incorporated into the ikura at 2 inoculum levels (log 2.4 and 4.2 CFU/g), and stored at 3 or 7 °C for up to 30 d. L. monocytogenes was recovered by plating onto modified Oxford media. Aerobic microflora were analyzed on plate count agar. Samples were tested at 0, 5, 10, 20, and 30 d. L. monocytogenes did not grow in chum salmon ikura held at 3 °C during 30 d at any salt level tested; however, the addition of salt at these levels did little to inhibit Listeria growth at 7 °C and counts reached 5 to 6 logs CFU/g. Components in the salmon egg intracellular fluid appear to inhibit the growth of L. monocytogenes. Total aerobic microflora levels were slightly lower in products with higher salt contents. These results indicate that temperature control is critical for ikura and similar products, but that products with lower salt contents can be safe, as long as good refrigeration is maintained.     

CHARACTERIZATION OF SALMON (ONCORHYNCHUS KETA) AND STURGEON (ACIPENSER TRANSMONTANUS) CAVIAR PROTEINS

The solubility of protein components in salmon (Oncorhynchus keta) and sturgeon (Acipenser transmontanus) caviar in distilled water, 5% (w/v) NaCl, 70% (v/v) ethanol at 65C, and 0.2% (w/v) NaOH was determined. The salt soluble proteins were the predominant fraction and constituted 84.2% of the recovered protein in salmon and 86.1% in sturgeon samples. The two most prominent protein fractions (12.5% sodium dodecyl sulfate‐polyacrylamide gel electrophoresis) for sturgeon caviar are most likely vitellin (96 kDa) and ovomucoid or phosvitin (28 kDa). For salmon roe, small proteins, possibly lysozyme or phosvitin (10 kDa), are also present.     

A rapid spectroscopic method for quantification of P57 in Hoodia gordonii raw material

The feasibility of quantifying the perceived active ingredient (P57) in Hoodia gordonii raw material using Fourier transform near- and mid-infrared spectroscopy combined with chemometric techniques was investigated. The concentration of P57 (a triterpene glycoside) was determined in 146 plant samples with liquid chromatography coupled to mass spectrometry and these values were used to develop a calibration model based on the partial least squares projections to latent structures (PLS) and orthogonal projections to latent structures (O-PLS) regression algorithms. The performance of each calibration model was evaluated according to the root mean square error of prediction (RMSEP) and correlation coefficient (R²). The PLS model with 2nd derivative pre-processing predicted P57 content based on the FT-NIR spectra with the best accuracy and a correlation coefficient (R²) value of 0.9629 and the lowest RMSEP of 0.03%. These results demonstrated that FT-NIR spectroscopy can be used to rapidly quantify P57 in H. gordonii raw material with high accuracy.     

Effects of brine concentration and temperature on equilibrium distribution coefficients during osmotic dehydration of sardine sheets

Effects of brine concentration (0.15-0.27 gNaCl/g) and temperature (32-38°C) on equilibrium distribution coefficients of sardine sheets were studied during osmotic dehydration. The equilibrium distribution coefficients of water and salt were determined using the mass transfer dynamic. The distribution coefficient for water and salt ranged from 0.5008 to 0.6254 and from 0.5286 to 0.7783, respectively. At a constant brine concentration, distribution coefficient of water decreased and distribution coefficient of salt increased with the increasing temperature. At a constant temperature, distribution coefficients of water and salt decreased with the increase in brine concentration. A multiple linear regression fitted (P=0.001; R²=0.877-0.966) distribution coefficient as a function of brine concentration and temperature.     

Relevance of season and nucleotide catabolism on changes in fillet quality during chilled storage of raw Atlantic salmon (Salmo salar L.)

Farmed 5–6 kg Atlantic salmon were pre-rigor filleted, vacuum packed and subsequently analysed after 1, 9 and 13 days of storage at 4 °C. The study was repeated in February, April, August and October. The conversion of hypoxanthine (Hx) showed the highest seasonal variation among the nucleotide metabolites, with an inverse relationship with sea temperature at harvesting (R² = 0.95–0.96). The Hx content was inversely related to fresh odour and flavour (R² = 0.81–0.83), but positively to tenderness (R² = 0.87). Hence, these results suggest that salmon reared in seawater of 11–15 °C (August–October) maintain a superior sensory quality for a longer period post-mortem than salmon reared at 6–8 °C (February–April). The colour intensity increased from days 1 to 9 post-mortem, probably due to rigor contraction. The highest increase in drip loss was observed in October and the lowest in April. It is proposed that seawater temperature significantly influences the storage life of raw salmon, and that Hx is a valuable biomarker for sensory quality.