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Non-destructive near infrared spectroscopy externally validated using large number sets for creation of robust calibration models enabling prediction of apple firmness
Journal of Near Infrared Spectroscopy ( IF 1.8 ) Pub Date : 2022-02-28 , DOI: 10.1177/09670335211054299
Martina Marečková 1 , Veronika Danková 1 , Lubor Zelený 1 , Pavol Suran 1
Affiliation  

Non-invasive flesh firmness prediction using near infrared spectroscopy has been perfected and validated on three apple varieties. Three novel calibration models were developed following three year's of repeated large-scale sampling of stored commercial apple varieties ‘Gala’, ‘Red Jonaprince’ and ‘Jonagored’. The spectroscopic results were compared directly with those obtained using the invasive method. Increased accuracy of calibration models was achieved with the newly established data collection model. The results exhibited coefficient of determination for calibration, R2, and ratio of prediction to deviation (RPD) in excess of 0.91 and 2.3, respectively, thus enabling excellent prediction of flesh firmness via a non-invasive and fast spectroscopic approach. The highest R2 obtained was 0.94, RPD 2.6 root mean square error of calibration 5.87 N, and root mean square error of cross-validation (internal) 6.75 N were found for variety ‘Red Jonaprince’. Our complex long-term study provided excellent external validated calibration models and the approach can help developing calibration models for commercial sorting lines using near infrared spectroscopy.



中文翻译:

使用大量集合进行外部验证的非破坏性近红外光谱,以创建能够预测苹果硬度的稳健校准模型

使用近红外光谱的非侵入性果肉硬度预测已在三个苹果品种上得到完善和验证。在对储存的商业苹果品种“Gala”、“Red Jonaprince”和“Jonagored”进行三年反复大规模采样后,开发了三种新的校准模型。光谱结果直接与使用侵入性方法获得的结果进行比较。新建立的数据收集模型提高了校准模型的准确性。结果显示,校准的确定系数 R 2和预测偏差比 (RPD) 分别超过 0.91 和 2.3,从而能够通过非侵入性和快速光谱方法出色地预测肉的硬度。最高 R 2对于品种“Red Jonaprince”,获得的结果为 0.94,RPD 2.6 校准的均方根误差为 5.87 N,交叉验证(内部)的均方根误差为 6.75 N。我们复杂的长期研究提供了出色的外部验证校准模型,该方法可以帮助开发使用近红外光谱的商业分拣线的校准模型。

更新日期:2022-02-28
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