基于共聚焦拉曼光谱技术的苹果轻微损伤早期判别分析

doi:10.3964/j.issn.1000-0593(2018)02-0430-06

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Abstract Mechanical damage of apple can result from compression, vibrations and collisions during harvesting, handling, transport operation and storing process. The part of tiny bruise apple is unable to be identified by the naked eye and is more likely to be invaded by micro-organism and pathogen, which will not only cause the affected fruit to rot, but will also affect other intact fruit. Therefore, it is significant for the postharvest treatment and storage to a identify the early tiny bruise of apple quickly and accurately, which can reduce economic losses. Raman spectroscopy combined with chemometric methods was used to rapidly classify apple flesh with early tiny bruising. SG (Savitzky-Golay) was used to smooth spectroscopy. AirPLS (adaptive iteratively reweighted penalized least squares) was used to correct the baseline of spectroscopy. After using KS method to divide training set and verification set, classified models were developed with non-linear support vector machine (SVM) regression which were based on the linear and polynomial kernel functions. The classification accuracy rate was 97.8%. The results showed that Raman spectroscopy combined with chemometric methods can quickly identify the early tiny bruise of apple, demonstrating the application prospect of Raman spectroscopy to discriminate the early tiny bruise apple.

Key words：Apple；Early tiny bruise；Raman spectroscopy；SVM

Received: 2017-05-10 Accepted: 2017-10-26

ZTFLH:

TP391.41

Corresponding Authors: TAN Zuo-jun E-mail: tzj@mail.hzau.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)02-0430-06 OR https://www.gpxygpfx.com/EN/Y2018/V38/I02/430

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