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Highlights • Weight, total fiber, and ash can be predicted by NIRS for whole lentils. • Size, moisture, and total fat can be predicted by NIRS for ground lentils. • Total raw protein can be predicted by NIRS for whole and ground lentils. • DPLS correctly classified 95% of samples as belonging to a PGI. Abstract Calibration statistical descriptors for both whole and ground lentils using Near Infrared Spectroscopy (NIRS), combined with fiber-optic probe, are presented and discussed. The models were developed for estimating the weight, size, total raw protein, moisture, total fat, total fiber, and ash. Standard methods were used to determine compositional parameters of 42 samples of different varieties of lentils. The calibration curves show a wide range of validity for all parameters. The results showed excellent predictability for the determination of weight, fiber, and ash in whole lentils. However, size, moisture, and total fat were predicted satisfactorily in ground lentils. The total protein content could be predicted for both whole and ground lentils. Moreover, NIRS and Direct Partial Least Squares (DPLS) were used to determine whether a sample of lentils belonged to the Protected Geographical Indication (PGI) "Lenteja de La Armuña" or not. The results showed that 95% of the samples were correctly classified as belonging to a PGI. This result demonstrates that this technique allows the differentiation of samples from nearby regions. [ABSTRACT FROM AUTHOR]

[Display omitted] • A total of 57 samples were analyzed including brown, green, black and red lentils. • MPLS models from NIR data allow predict Ca, Fe and Mg for ground lentils. • 15 individual fatty acid of lentil can be predicted by NIRS for ground lentils. • NIR reduces analysis time and does not require the use of solvents. Lentils are an important source of both macro- and micronutrients. Their fat content is relatively low and is composed of mainly polyunsaturated fatty acids. The minerals found in lentils are mainly magnesium, potassium and iron. This study investigates the use of near infrared reflectance spectroscopy (NIRS) to predict the mineral content and fatty acid profile of lentil seeds (Lens culinaris Medicus). Samples (57) of brown, green, black and red lentils were analysed, and their mineral (calcium, iron and magnesium) and fatty acid contents were determined. NIR spectra for whole intact samples and after the samples were ground into powder were obtained, and the two recording methods were compared. The different compounds were predicted using the modified partial least squares regression method. The predictive models developed show excellent coefficients of determination (RSQ > 0.9) for the C 16:0, C 18:2, C 20:5n-3, C 21:0, omega 6 and calcium parameters. The results obtained reveal that it is possible to predict the fatty acid and mineral composition of lentils using near infrared spectroscopy. Furthermore, the results obtained show that the equations obtained can be applied to unknown lentil samples. [ABSTRACT FROM AUTHOR]