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Temperature fluctuation commonly occurs in the cold chain leading to complete or partial thawing and refreezing of frozen products resulting in a multifrozen product. Such oscillation of temperature could cause significant quality reduction compared to single frozen products. This study was designed to differentiate frozen Atlantic salmon fillets based on the level of temperature fluctuation. Near‐infrared spectroscopy (NIRS) coupled with chemometrics was used to classify the frozen fillets stored at no fluctuation (NF), low fluctuation (LF), high fluctuation (HF), and very high fluctuation (VF) temperature. Using spectral profiles obtained at both frozen and thawed states, fillets were classified based on the level of temperature fluctuation by partial least squares discriminant analysis (PLS‐DA). The thawed samples showed better classification accuracy (71%) than frozen samples (66%) in a four‐class model. Considering the small variation within the first two (NF, LF) and the last two (HF, VF) groups, a two‐class classification model was developed using thawed samples, and the obtained model correctly classified the two groups ([NF, LF] and [HF, VF]) with 100 % classification accuracy. Protein‐ and water‐related changes were found important to distinguish the fillets. Based on these findings, the four‐class prediction model is found insufficient to be used for nondestructive determination of temperature history of frozen fillets. However, the two‐class prediction model with further external validation can be applied to determine the level of temperature fluctuation particularly using fillets scanned at thawed state. Practical Application: NIR spectroscopy can be used to evaluate the degree of temperature fluctuation and thus related quality loss throughout the logistics of frozen Atlantic salmon fillets. Researchers, food control authorities, and the retail industry could be the primary beneficiaries of this research output. [ABSTRACT FROM AUTHOR]

Spectral data from multiple sources can be integrated into multi-block fusion chemometric models, such as sequentially orthogonalized partial-least squares (SO-PLS), to improve the prediction of sample quality features. Pre-processing techniques are often applied to mitigate extraneous variability, unrelated to the response variables. However, the selection of suitable pre-processing methods and identification of informative data blocks becomes increasingly complex and time-consuming when dealing with a large number of blocks. The problem addressed in this work is the efficient pre-processing, selection, and ordering of data blocks for targeted applications in SO-PLS. We introduce the PROSAC–SO–PLS methodology, which employs pre-processing ensembles with response-oriented sequential alternation calibration (PROSAC). This approach identifies the best pre-processed data blocks and their sequential order for specific SO-PLS applications. The method uses a stepwise forward selection strategy, facilitated by the rapid Gram-Schmidt process, to prioritize blocks based on their effectiveness in minimizing prediction error, as indicated by the lowest prediction residuals. To validate the efficacy of our approach, we showcase the outcomes of three empirical near-infrared (NIR) datasets. Comparative analyses were performed against partial-least-squares (PLS) regressions on single-block pre-processed datasets and a methodology relying solely on PROSAC. The PROSAC–SO–PLS approach consistently outperformed these methods, yielding significantly lower prediction errors. This has been evidenced by a reduction in the root-mean-squared error of prediction (RMSEP) ranging from 5 to 25 % across seven out of the eight response variables analyzed. The PROSAC–SO–PLS methodology offers a versatile and efficient technique for ensemble pre-processing in NIR data modeling. It enables the use of SO-PLS minimizing concerns about pre-processing sequence or block order and effectively manages a large number of data blocks. This innovation significantly streamlines the data pre-processing and model-building processes, enhancing the accuracy and efficiency of chemometric models. Summary of the PROSAC–SO–PLS methodology applied to one of the response variables (protein) of the miniS-milk dataset, which is one of the three datasets presented in this study. In this specific case, multiple blocks of spectral data derived from multiple miniature spectrometers measuring the same milk samples simultaneously are subjected to an ensemble of pre-processing techniques within the PROSAC framework. The five most variance-explaining blocks identified by PROSAC are subsequently utilized as input variables for SO-PLS, facilitating the construction of a model that optimizes block utilization for predicting the protein content in milk, with protein content reference values obtained from laboratory analyses. [Display omitted] • PROSAC and SO-PLS are combined for improved multi-block data fusion. • PROSAC streamlines SO-PLS by easing pre-processing and block order concerns. • PROSAC–SO–PLS effectively manages a large number of input data blocks. • PROSAC–SO–PLS is tested on three NIR datasets, surpassing existing state-of-art. • PROSAC–SO–PLS reduces prediction errors by 5–25 % for the evaluated NIR datasets. [ABSTRACT FROM AUTHOR]

• A fiber-optic spatially-resolved spectroscopy (SRS) setup measured milk NIR spectra. • The setup evaluated 30 different illumination-to-detection distances (1.1–4 mm) • Shorter illumination-to-detection distances were optimal for lactose prediction. • Larger illumination-to-detection distances were optimal for fat and protein. • The accuracy of the composition predictions are well within the ICAR requirements. To support in-line quality control of raw milk and ensure the close monitoring of the metabolic and udder health of dairy cows, we propose a fiber-optic spatially-resolved spectroscopy (SRS) setup. This setup allows to vary the interaction of long-wave near-infrared (LW-NIR) light with the milk by submerging a separate optical illumination and detection fiber into the sample and altering their relative distance to optimize measurements for specific milk component(s). We evaluated this approach for predicting milk fat, protein, and lactose content and determined the optimal illumination-to-detection distances for each milk component. The region between 1.1 and 1.8 mm was optimal for lactose, and between 2.2 and 3.8 mm for fat and protein. These distance ranges resulted in a root-mean-square error of prediction (RMSEP) of less than 0.10 % weight/weight (wt/wt) for milk fat, and lower than 0.13 % (wt/wt) for protein and lactose. Integration of these distances into a fiber-optic SRS reflectance probe would allow to simultaneously determine the fat, protein, and lactose content of raw milk in-line with high accuracy. [ABSTRACT FROM AUTHOR]

The sugar profile of a syrup determines its suitability for use in different food applications. The traditional wet chemical method for sugar profile analysis is time-consuming, destructive, and requires several chemical reagents. Therefore, this study aimed to develop a rapid method for determining the concentration of the main sugars (glucose, maltose, maltotriose, and fructose) in tapioca syrups using NIR spectroscopy in the 12000–4000 cm−1 wavenumber region or 800–2500 nm wavelength range. As large spectral variation due to water and liquid glucose was observed, orthogonal projection (OP) was evaluated to reduce those effects. Results showed that for all sugars studied, orthogonal projection prior to partial least squares regression (PLSR) improved the prediction performance in terms of the root mean square error of prediction (RMSEP) by 5–47% and the coefficient of determination for prediction (R p 2) by 3–7%. The developed models had high R p 2 and low RMSEP values, which provides opportunities for industrial monitoring of the sugar profiles of tapioca syrup products. • NIR spectroscopy allows quantifying many sugars in tapioca syrups. • Interference removal by orthogonal projection enhances prediction of syrup sugars. • High-accuracy prediction models for the individual sugars were built by using PLSR. • Identification of important wavelengths for syrup sugar profiling was established. [ABSTRACT FROM AUTHOR]

• Bean type had a significant effect on tannins contents. • Phytate to iron and phytate to zinc molar ratios of raw beans were high. • NIR spectroscopy predicted antinutrient to mineral molar ratios of raw beans. • Ability to predict antinutrient influenced prediction of molar ratios using NIR. The presence of antinutrients in common beans negatively affects mineral bioavailability. Therefore, this study aimed to predict the antinutrient to mineral molar ratios (proxy-indicators of in vitro mineral bioavailability) of a wide range of raw bean types, using near-infrared (NIR) spectroscopy. Iron, zinc, phytate and tannin concentrations and, antinutrient to mineral molar ratios were determined. Next, model calibration using NIR spectra from milled beans was performed. This entailed wavelength selection, pre-processing and partial least squares regression. Bean type had a significant effect on tannin content. The average values of phytate to iron (Phy:Fe), phytate to zinc (Phy:Zn), tannins to iron (Tan:Fe) and phytate and tannins to iron (Phy + Tan:Fe) MRs were 27.6, 61.7, 16.0 and 43.6, respectively. With determination coefficients for test set prediction above 75%, the PLS-R models for Phy:Zn, Tan:Fe and Phy + Tan:Fe molar ratios are useful for screening purposes. [ABSTRACT FROM AUTHOR]