Non-destructive quality classification of rice taste properties based on near-infrared spectroscopy and machine learning algorithms.

The taste quality of rice is determined by protein and amylose percentages, with low levels indicating high-quality taste in Japan. However, accurate non-destructive screening remains a challenge for the industry. We explored the use of machine learning models and near-infrared spectra to classify rice taste quality.

Non-destructive assessment of amylose content in rice using a quality inspection system at grain elevators.

Amylose content is an important determinant of rice quality. Its accurate, non-destructive assessment is a challenge to the industry. We examined whether the automatic quality inspection system accurately measured the amylose content.

Combined analysis of near-infrared spectra, colour, and physicochemical information of brown rice to develop accurate calibration models for determining amylose content.

Amylose content is an important determinant of rice quality. Accurate non-destructive determination of amylose content remains a primary challenge for the rice industry. Here, we analysed the accuracy of three models for the non-destructive determination of amylose content.