[Research on the soluble solids content of pear internal quality index by near-infrared diffuse reflectance spectroscopy].
Yande Liu, Xudong Sun, Xingmiao Chen
Abstract
Yande Liu, Xudong Sun, Xingmiao Chen
Abstract
The objectives of the present study were to establish the relationships between the near-infrared diffuse reflectance (NIR) spectra and the soluble solids content (SSC) of internal quality index of pear fruit, and to evaluate the value of NIR spectrometry in measuring the SSC of internal quality index of pear fruit. NIR spectroscopy in the 350-1 800 nm range was used to analyze the SSC of internal quality index of pear fruit with multi-linear regression (MLR), principal component regression (PCR) and partial least square (PLS) regression. Meanwhile, the best combinations of different positions at pear fruit, the logarithms of the reflectance reciprocal log(1/R), its first derivative D1 log(1/R) and second derivative D2 log(1/R) were investigated. The best prediction results, based on the comparative analysis, were obtained with the PLS model and D1 log(1/R) at equatorial position of pear fruit. The results show that the predictions with PLS models, based on D1 log(1/R) at equatorial position of pear fruit, are correlation coefficients (R(p)) of 0.851 7 and root mean standard error of prediction (RMSEP) of 0.879 3 for SSC. The preliminary research on the built models indicates that NIR spectroscopy could provide an accurate, reliable and nondestructive method for assessing the SSC of internal quality index of pear fruit.
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The objectives of the present study were to establish the relationships between the near-infrared diffuse reflectance (NIR) spectra and the soluble solids content (SSC) of internal quality index of pear fruit, and to evaluate the value of NIR spectrometry in measuring the SSC of internal quality index of pear fruit. NIR spectroscopy in the 350-1 800 nm range was used to analyze the SSC of internal quality index of pear fruit with multi-linear regression (MLR), principal component regression (PCR) and partial least square (PLS) regression. Meanwhile, the best combinations of different positions at pear fruit, the logarithms of the reflectance reciprocal log(1/R), its first derivative D1 log(1/R) and second derivative D2 log(1/R) were investigated. The best prediction results, based on the comparative analysis, were obtained with the PLS model and D1 log(1/R) at equatorial position of pear fruit. The results show that the predictions with PLS models, based on D1 log(1/R) at equatorial position of pear fruit, are correlation coefficients (R(p)) of 0.851 7 and root mean standard error of prediction (RMSEP) of 0.879 3 for SSC. The preliminary research on the built models indicates that NIR spectroscopy could provide an accurate, reliable and nondestructive method for assessing the SSC of internal quality index of pear fruit.
Key concepts: PEAR, Partial least squares regression, Near-infrared spectroscopy, Linear regression, Mean squared error, Chemistry, Diffuse reflectance infrared fourier transform, Regression analysis