[Comparison of PLS and SMLR for nondestructive determination of sugar content in honey peach using NIRS]

Hui-Rong Xu, Hui-Jun Wang, Kang Huang, Yi-Bin Ying, Cheng Yang, Hao Qian, Jun Hu
Guang Pu Xue Yu Guang Pu Fen Xi, Guang Pu 2008, 28 (11): 2523-6
Nondestructive fruit quality assessment in packing houses can be carried out using near infrared (NIR) spectroscopy. However, in industrial process, some experimental conditions (e. g. temperature, fruit variety) cannot be strictly controlled and their changes would reduce the robustness of the NIR-based models. In the present paper, a total of 100 honey fruits from two super markets were used as experimental materials. Fifty honey fruits were stored at room temperature and the other fifty samples were stored at 0-4 degrees C. NIR diffuse reflectance spectra of the honey peaches were measured in the spectral range of 4 000-2 500 cm(-1) using InGaAs detector. After outlier diagnosis using leverage values and Dixon test and spectra data pretreatment with Norris derivative filter (segment length: 5, gap: 5), partial least square (PLS) regression with standard normal variate (SNV) transformation and stepwise multilinear regression (SMLR) with multiplicative scatter correction (MSV) were used to establish calibration models based on first derivative spectra. Comparing the two calibration methods of PLS and SMLR, the performances of the models developed by SMLR were found much better than that by PLS method. The best results for PLS models were: correlation coefficient of calibration (R(c)) = 0.965, root mean square errors of calibration (RMSEC) = 0.3010 Brix, correlation coefficient of cross-validation (R(cv)) = 0.812, root mean square errors of cross-validation (RMSECV) = 0.67 degrees Brix and ratio of standard deviation to root mean square errors of cross-validation (RPD) = 1.72, which were slightly worse than those for SMLR: R(c) = 0.929, RMSEC = 0.424 degrees Brix of calibration and R(cv) = 0.887, RMSECV = 0.532 degrees Brix of cross-validation and RPD = 2.16. The RPD values for SMLR models in three different spectral regions 4 290-7 817, 7 817-10 725 and 4 290-10 725 cm(-1) were: 1.97, 1.89 and 2.16, respectively. The performance of the model developed by SMLR in the 4 290-7 817 cm(-1) region was much better than that in the 7 817-10 725 cm(-1) region. The results indicated that the SMLR method could develop a good calibration model by selecting wavelengths insensitive to temperature and NIR spectra could be used for sugar content prediction of fruit samples with varied temperature when developing a global robust calibration model to cover the temperature range.

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