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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]

Abstract: The effect of cultivar, season, shelf-life and origin on the accuracy of near infrared (NIR) calibration models for the soluble solids content (SSC) and firmness of apple was studied based on a large spectral data set based on approximately 6000 apple fruit from different cultivars, origins, shelf-life exposure time and seasons. To interpret the variance in the spectra with respect to biological variability, functional analysis of variance (FANOVA) was used. From the FANOVA analysis it was concluded that the effects of cultivar, origin and shelf-life exposure time on the NIR spectra were all significant. The largest differences in the spectra were found around the water absorption peaks (970, 1170 and 1450nm). External validations using independent data sets showed that the accuracy of the models increased considerably when more variability was included in the calibration data set. In general the RMSEP for predictions of the SSC were in the range 0.6–0.8°Brix, while for Magness Taylor firmness it was 5.9–8.8N, depending on the cultivar. It was shown that atypical data can lead to large validation errors. It is, therefore, important to collect a calibration data set which is sufficiently representative for future samples to be analyzed with the developed calibration models and to develop simple procedures for model adaptation during practical use. [Copyright &y& Elsevier]

Abstract: Continuous wave (CW) and time-resolved (TRS) near infrared (NIR) reflectance measurements were carried out on ‘Conference’ pear fruit. While the absorption coefficient spectra obtained from the TRS measurements were dominated by the overtone of the hydroxyl moiety at 975nm, the scattering coefficient spectra were almost flat. The spectral data were used to construct calibration models for the soluble solids content (SSC) and Magness Taylor firmness of ‘Conference’ pear fruit. Reasonable models for SSC (RMSEP=0.44 °Brix) were obtained when the CW spectra in the range 780–1700nm were used, but not when the range was limited to that of the TRS equipment (875–1030nm). No satisfactory calibration model could be established between the TRS absorption coefficient spectrum and SSC. With none of the spectroscopic techniques could significant calibration models for firmness be constructed, although a highly nonlinear relationship between scattering coefficients at 900nm and firmness was observed. [Copyright &y& Elsevier]