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Near-infrared (NIR) spectroscopy technique has been developed to objectively measure quality attributes for different food products in recent years. However, any change in measurement condition or instrumental response can result in performance decline of the multivariate calibration model. Thus, calibration maintenance is significant for widespread application of NIR technique in food analysis. Various maintenance approaches have been performed to improve the applicability of calibration model. This paper reviews common calibration maintenance methods based on different principles, and introduces multivariate statistical evaluations for model acceptance. Calibration maintenance applications in food quality analysis are presented to illustrate the capability of this approach for food quality prediction, which can improve the model applicability and prediction accuracy effectively. Further researches of calibration maintenance for different food quality parameters are needed to realize efficient and reliable application of NIR prediction in food industry.

Calibration Maintenance Application of Near-infrared Spectrometric Model in Food Analysis