Posom Jetsada, Klaprachan Junjira, Rattanasopa Kamonpan, Sirisomboon Panmanas, Saengprachatanarug Khwantri, Wongpichet Seree
Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand.
Department of Agricultural Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
ACS Omega. 2020 Oct 19;5(43):27909-27921. doi: 10.1021/acsomega.0c03203. eCollection 2020 Nov 3.
Handheld near-infrared spectroscopy was used to study the effect of integration time and wavelength selection on predicting marian plum quality including soluble solids content (SSC), the potential of hydrogen ion (pH), and titratable acidity (TA). For measurements representing actual conditions, the on-tree fruits were scanned under in-field conditions. The assumption was that the robust model might be achieved when the models were developed under actual conditions. The results of the main effect test show that the integration time did not statistically affect SSC, pH, and TA predictions (-value > 0.05) and the wavelength range had a significant impact on prediction (-value < 0.01). An integration time of 30 ms coupled with a wavelength range of 670-1000 nm was the optimal conditions for the SSC prediction, while an integration time of 20 ms with 670-1000 nm wavelength was optimal for pH and TA prediction because of the lowest root-mean-square error of cross-validation (RMSECV). The optimal models for SSC, pH, and TA could be improved using spectral pre-processing of multiplicative scatter correction. The effective models for SSC, pH, and TA improved and reported the coefficients of determination ( ) and root-mean-square errors of prediction (RMSEP) of 0.66 and 0.86 °Brix; 0.79 and 0.15; and 0.71 and 1.91%, respectively. The SSC, pH, and TA models could be applied for quality assurance. These models benefit the orchardist for on-tree measurement before harvesting.
采用手持式近红外光谱技术研究积分时间和波长选择对预测人心果品质（包括可溶性固形物含量（SSC）、氢离子电位（pH）和可滴定酸度（TA））的影响。为了进行代表实际情况的测量，在田间条件下对树上的果实进行扫描。假设是当在实际条件下建立模型时，可能会获得稳健的模型。主效应测试结果表明，积分时间对SSC、pH和TA预测没有统计学影响（P值>0.05），而波长范围对预测有显著影响（P值<0.01）。30 ms的积分时间与670 - 1000 nm的波长范围相结合是预测SSC的最佳条件，而20 ms的积分时间与670 - 1000 nm波长相结合是预测pH和TA的最佳条件，因为交叉验证的均方根误差（RMSECV）最低。使用乘法散射校正的光谱预处理可以改进SSC、pH和TA的最佳模型。SSC、pH和TA的有效模型得到改进，并分别报告了决定系数（R²）和预测均方根误差（RMSEP）为0.66和0.86 °Brix；0.79和0.15；以及0.71和1.91%。SSC、pH和TA模型可用于质量保证。这些模型有助于果园工人在收获前进行树上测量。
