1. Portable LWNIR and SWNIR spectroscopy with pattern recognition technology for accurate and nondestructive detection of hidden mold infection in citrus.
- Author
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Li, Pao, Su, Guanglin, Du, Guorong, Jiang, Liwen, Dong, Yiqing, and Shan, Yang
- Subjects
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PATTERN recognition systems , *NEAR infrared reflectance spectroscopy , *CITRUS , *FRUIT skins , *SUPPORT vector machines - Abstract
An accurate and nondestructive detection method of hidden mold infection in citrus was established based on portable near infrared diffuse reflectance spectroscopy and chemometric methods. [Display omitted] • Nondestructive detection of hidden mold infection in citrus was achieved. • NIRDRS light was demonstrated to penetrate the citrus peel. • Models were validated by the validation and external validation sets. • Identification models of LWNIR were much better than those of SWNIR. • Identification accuracy of LWNIR-DT-PLS-DA model was 100%. An accurate and nondestructive detection method of hidden mold infection in citrus was established based on portable near infrared diffuse reflectance spectroscopy (NIRDRS) and chemometric methods. Penetrability of NIRDRS light on the peel of Chunjian hybrid citrus was studied. The results show that NIRDRS light can penetrate the peel to a certain extent, while the penetrability of short-wave near infrared (SWNIR) was better than that of long-wave near infrared (LWNIR). The identification models of hidden mold infection were established with five pattern recognition methods combined with different wavelength bands. The results show that the identification models of LWNIR were much better than those of SWNIR. 100% identification accuracies of LWNIR were obtained with soft independent pattern classification (SIMCA), support vector machine (SVM), partial least squares discriminant analysis (PLS-DA) and the optimized pretreatment methods. In addition, the developed models were further validated by the external validation set collected one month later. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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