首页> 外文会议>Conference on Monitoring Food Safety, Agriculture, and Plant Health; Oct 29-30, 2003; Providence, Rhode Island, USA >Study on rapid valid acidity evaluation of apple by fiber optic diffuse reflectance technique
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Study on rapid valid acidity evaluation of apple by fiber optic diffuse reflectance technique

机译:光纤漫反射技术快速评估苹果的有效酸度

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Some issues related to nondestructive evaluation of valid acidity in intact apples by means of Fourier transform near infrared (FTNIR) (800-2631nm) method were addressed. A relationship was established between the diffuse reflectance spectra recorded with a bifurcated optic fiber and the valid acidity. The data were analyzed by multivariate calibration analysis such as partial least squares (PLS) analysis and principal component regression (PCR) technique. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influence of data preprocessing and different spectra treatments were also investigated. Models based on smoothing spectra were slightly worse than models based on derivative spectra and the best result was obtained when the segment length was 5 and the gap size was 10. Depending on data preprocessing and multivariate calibration technique, the best prediction model had a correlation efficient (0.871), a low RMSEP (0.0677), a low RMSEC (0.056) and a small difference between RMSEP and RMSEC by PLS analysis. The results point out the feasibility of FTNIR spectral analysis to predict the fruit valid acidity non-destructively. The ratio of data standard deviation to the root mean square error of prediction (SDR) is better to be less than 3 in calibration models, however, the results cannot meet the demand of actual application. Therefore further study is required for better calibration and prediction.
机译:解决了与通过傅立叶变换近红外(FTNIR)(800-2631nm)方法对完整苹果中有效酸度进行无损评估有关的一些问题。在用分支光纤记录的漫反射光谱与有效酸度之间建立了关系。通过多元校正分析,例如偏最小二乘(PLS)分析和主成分回归(PCR)技术,对数据进行了分析。总共对120个富士苹果进行了测试,其中80个用于形成校准数据集。还研究了数据预处理和不同光谱处理的影响。基于平滑光谱的模型比基于导数光谱的模型稍差一些,并且当段长为5且间隙大小为10时,可获得最佳结果。根据数据预处理和多元校准技术,最佳预测模型具有有效的相关性(0.871),较低的RMSEP(0.0677),较低的RMSEC(0.056),以及通过PLS分析得出的RMSEP和RMSEC之间的差异很小。结果表明,FTNIR光谱分析可以无损地预测水果的有效酸度。在校准模型中,数据标准偏差与预测的均方根误差(SDR)的比率最好小于3,但是结果不能满足实际应用的需求。因此,需要进行进一步的研究以更好地进行校准和预测。

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