Comparison of various chemometric analysis for rapid prediction of thiobarbituric acid reactive substances in rainbow trout fillets by hyperspectral imaging technique

Abstract This study explores the potential application of hyperspectral imaging (HSI; 430–1,010 nm) coupled with different linear and nonlinear models for rapid nondestructive evaluation of thiobarbituric acid‐reactive substances (TBARS) value in rainbow trout (Oncorhynchus mykiss) fillets during 12 days of cold storage (4 ± 2°C). HSI data and TBARS value of fillets were obtained in the laboratory. The primary prediction models were established based on linear partial least squares regression (PLSR) and least squares support vector machine (LS‐SVM). In full spectral range, the prediction capability of LS‐SVM (RP2 = 0.829; RMSEP = 0.128 mg malondialdehyde [MDA]/kg) was better than PLSR (RP2 = 0.748; RMSEP = 0.155 mg MDA/kg) model and LS‐SVM model exhibited satisfactory prediction performance (RP2 > 0.82). To simplify the calibration models, a combination of uninformative variable elimination and backward regression (UB) was used as variable selection. Nine wavelengths were selected. Various chemometric analysis methods including linear PLSR and multiple linear regression and nonlinear LS‐SVM and back‐propagation artificial neural network (BP‐ANN) were compared. The simplified models showed better capability than those were built based on the whole dataset in prediction of TBARS values. Moreover, the nonlinear models were preferred over linear models. Among the four chemometric algorithms, the best and weakest models were LS‐SVM and PLSR model, respectively. UB‐LS‐SVM model was the optimal models for predicting TBARS value in rainbow trout fillets (RP2 = 0.831; RMSEP = 0.125 mg MDA/kg). The establishing of lipid‐oxidation prediction model in rainbow trout fish was complicated, due to the fluctuations of TBARS values during storage. Therefore, further researches are needed to improve the prediction results and applicability of HIS technique for prediction of TBARS value in rainbow trout fish

Sensory evaluation of selected formulated milk barberry drinks using the fuzzy approach

Amid rigid competition in marketing to accomplish customers' needs, the cost of disappointment is too high. In an effort to escape market disappointment, one of the options to be considered is probing for customer satisfaction through sensory evaluation. This study aims to rank the six selected milk‐barberry drink formulae out of 24 (code numbers S3, S4, S15, S16, S17 and S18) each having different milk:barberry:pectin amount (7: 3: 0.2; 6: 4: 0.2; 7: 3: 0.4, 6: 4: 0.4, 5: 5: 0.4 and 6: 4: 0.4), respectively, and to determine the best of quality attribute through sensory evaluation, using the fuzzy decision‐making model. The selection was based on pH, total solid content, and degree of serum separation and rheological properties of the drinks. The results showed that the S4 had the highest acceptability, rated under the “very good” category, whereas the lowest acceptability was reported for the S3 which was classified under the “satisfactory” category. In summary, the ranking of the milk‐barberry drinks was S4 > S17 > S16 > S15 > S18 > S3. Furthermore, quality attributes were ranked as taste > mouth feel > aroma > color. Results suggest that the fuzzy approach could be appropriately used to evaluate this type of sensory data