Modeling and De-Noising for Nondestructive Detection of Total Soluble Solid Content of Pomelo by Using Visible/Near Infrared Spectroscopy

Author:

Xu Sai12,Lu Huazhong3,Liang Xin12,Ference Christopher4ORCID,Qiu Guangjun12ORCID,Fan Changxiang1

Affiliation:

1. Institute of Facility Agriculture of Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China

2. Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510640, China

3. Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China

4. USDA, Agricultural Research Service, US Pacific Basin Agricultural Research Center, 64 Nowelo Street, Hilo, HI 96720, USA

Abstract

The flavor of Pomelo is highly variable and difficult to determine without peeling the fruit. The quality of pomelo flavor is due largely to the total soluble solid content (TSSC) in the fruit and there is a commercial need for a quick but nondestructive TSSC detection method for the industrial grading of pomelo. Due to the large size and thick mesocarp of pomelo, determining the internal quality of a pomelo fruit in a nondestructive manner is difficult, and the detection accuracy is further complicated by the noise typically generated by the common methods for the internal quality detection of other fruits. Thus, the aim of this study was to determine the optimal method to accurately detect pomelo TSSC and find a de-noising model which reduces the influence of noise on the optimal method’s results. After developing a full-transmission visible/near infrared (VIS/NIR) spectroscopy sampling method, the confirming experimental results showed that the optimal pomelo TSSC detection model was Savitzky Golay + standard normal variate + competitive adaptive reweighted sampling + partial least squares regression. The R2 and RMSE of the calibration set for pomelo TSSC detection were 0.8097 and 0.8508, respectively, and the R2 and RMSE of the validation set for pomelo TSSC detection were 0.8053 and 0.8888, respectively. Both reference and dark de-noising are important for pomelo internal quality detection and should be calibrated frequently to compensate for time drift. This study found that large sensor response translation noise can be reduced with an artificial horizontal shift. Data supplementation is efficient for improving the adaption of the detection model for batch differences in pomelo samples. Using this optimized de-noising model to compensate for time drift, sensor response translation, and batch differences, the developed detection method is capable of satisfying the requirements of the industry (TSSC detection R2 was equal or larger than 0.9, RMSE was less than 1). These results indicate that full-transmission VIS/NIR spectroscopy can be exploited to realize the nondestructive detection of pomelo TSSC on an industrial scale, and that the methodologies used in this study can be immediately implemented in real-world production.

Funder

National key research and development program

Special fund for Rural Revitalization of Guangdong Province

Natural Science Foundation of Guangdong Province

Lingnan Modern Agriculture Project

New Developing Subject Construction Program of Guangdong Academy of Agricultural Science

Talent Training Program of Guangdong Academy of Agricultural Science

Science and Technology Cooperation Program of Foshan City and Young Talent Support Project of Guangzhou Association for Science and Technology

Publisher

MDPI AG

Subject

Plant Science,Health Professions (miscellaneous),Health (social science),Microbiology,Food Science

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