The use of near infrared reflectance spectroscopy (NIRS) was investigated as an alternative method for predicting moisture (M), oil, crude protein (CP), ash, salt as NaCl, total volatile nitrogen (TVN) and buffer capacity in fishmeal. The NIRS calibration models were developed using the modified partial least squares (MPLS) regression technique. One thousand and ten (n ¼ 1010) fishmeal samples were used to predict chemical composition for quality control in the fishmeal industry. Equations were selected based on the lowest cross validation errors (SECV). The coefficient of determination in calibration (R 2 ) and SECV were 0.93 and 3.9 g kg -1 dry matter (DM); 0.85 and 5.7 g kg -1 DM; 0.92 and 3.7 g kg -1 DM; 0.91 and 4.7 g kg -1 DM; 0.88 and 6.7 g kg -1 DM; 0.94 and 1.8 g kg -1 DM; for M, CP, oil, ash, TVN and NaCl, respectively. It was concluded that NIRS can be used as a method to monitor the quality of fishmeal under industrial conditions. KEY WORDS
Near-infrared reflectance (NIR) spectroscopy combined with chemometrics was used to identify and authenticate fishmeal batches made with different fish species. Samples from a commercial fishmeal factory (n = 60) were scanned in the NIR region (1100-2500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), dummy partial least-squares regression (DPLS), and linear discriminant analysis (LDA) based on PCA scores were used to identify the origin of fishmeal produced using different fish species. Cross-validation was used as validation method when classification models were developed. DPLS correctly classified 80 and 82% of the fishmeal samples. LDA calibration models correctly classified >80% of fishmeal samples according to fish species The results demonstrated the usefulness of NIR spectra combined with chemometrics as an objective and rapid method for the authentication and identification of fish species used to manufacture the fishmeal.
The objective of this study was to explore the use of near infrared (NIR) spectroscopy and chemometrics to monitor the degree of heat treatment of fish meal. Six batches of fish meal (approximately 500 g) were split in sub-samples of 50 g and heated at constant temperature (60 ± 5°C) for different periods of time (15 and 30 min, 1, 2, 3, 4, 5, 6, 8, 48 and 72 h) in a force air oven and scanned in the NIR region (1100-2500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), stepwise discriminant analysis (SLDA) and partial least square regression (PLS) models were used to interpret, classify and predict the extent to heat treatment in fish meal samples. The SLDA models correctly classified 80% and 100% of fish meal samples belonging to the untreated fish meal and after 4, 5 and 6 h of heat treatment. However, samples heated for 30 min, 1, 2 and 3 h yield poor classification rates (less than 50%). This study demonstrated the potential ability of NIR spectroscopy to predict and classify the extent of heat treatment during the production of fish meal. However, further research must carry out in order to validate the NIR calibrations to predict the degree of heat treatment in fish meal expose to a shorter time.
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