Composition assessment of sous vide beef meat by near-infrared spectroscopy based on compact spectrophotometers, multivariate regression, and jack-knife variable selection

‘Sous vide’ is a cooking process that uses long time and low temperatures, affecting the quality and composition of the product. This study aimed to develop and validate prediction models for the proximate composition of sous vide beef tenderloin using near-infrared reflectance spectroscopy (NIRS) and to compare the performance of two portable NIR spectrometers. Original spectra were pretreated using the Standard Normal Variate (SNV) method, followed by the 1st derivative. Models using partial least squares regression (PLS) were constructed with all spectral variables, and after the jack-knife algorithm performed wavelength selection. The centesimal composition of the ground samples was accurately determined by the models, except for the carbohydrate content. The prediction errors resulting from external validation (RMSEP) for the InnoSpectra and NeoSpectra evaluated compact instruments were, respectively, 0.92% and 0.65% for moisture, 0.72% and 0.93% for fat, 0.93% and 0.60% for protein, and 0.12% and 0.03% for ash. Conversely, poor results were obtained for samples where readings were taken from whole roasters with or without packaging. A new method for assessing the quality and usefulness of multivariate models was proposed based on RMSEP and comparing the distributions of the reference and validation data. The NIRS-based method is fast, requires simple sample preparation, does not require the use of chemicals, and employs low-cost instruments.