Reconsidering the Need for Empirical Alignment and Wavelength Calibration Steps in the Building of a Dispersive NIR Spectrometer with an Application for Ethanol Quantification Using a Polymer Filament 3D Printer.

The construction of a dispersive optical spectrometer using three-dimensional (3D) design software and printing, without applying any optical adjustments, its validation, and application to quantification of ethanol in multiproduct liquids, is the objective of this work. A 3D design software was used to design a near-infrared (NIR) spectrometer in the region from 800 to 1600 nm from the dimensions of commercially available optical components. The project was printed on a polymer filament 3D printer, and the components were fitted to the printed part.

FT-NIR and linear discriminant analysis to classify chickpea seeds produced with harvest aid chemicals.

Spectroscopy and machine learning (ML) algorithms have provided significant advances to the modern food industry. Instruments focusing on near-infrared spectroscopy allow obtaining information about seed and grain chemical composition, which can be related to changes caused by field pesticides. We investigated the potential of FT-NIR spectroscopy combined with Linear Discriminant Analysis (LDA) to discriminate chickpea seeds produced using different desiccant herbicides at harvest anticipation.