Xu Sai, Lu Huazhong, Liang Xin, Ference Christopher, Qiu Guangjun, Fan Changxiang
Institute of Facility Agriculture of Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China.
Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510640, China.
Foods. 2023 Aug 6;12(15):2966. doi: 10.3390/foods12152966.
The flavor of Pomelo is highly variable and difficult to determine without peeling the fruit. The quality of pomelo flavor is due largely to the total soluble solid content (TSSC) in the fruit and there is a commercial need for a quick but nondestructive TSSC detection method for the industrial grading of pomelo. Due to the large size and thick mesocarp of pomelo, determining the internal quality of a pomelo fruit in a nondestructive manner is difficult, and the detection accuracy is further complicated by the noise typically generated by the common methods for the internal quality detection of other fruits. Thus, the aim of this study was to determine the optimal method to accurately detect pomelo TSSC and find a de-noising model which reduces the influence of noise on the optimal method's results. After developing a full-transmission visible/near infrared (VIS/NIR) spectroscopy sampling method, the confirming experimental results showed that the optimal pomelo TSSC detection model was Savitzky Golay + standard normal variate + competitive adaptive reweighted sampling + partial least squares regression. The R and RMSE of the calibration set for pomelo TSSC detection were 0.8097 and 0.8508, respectively, and the R and RMSE of the validation set for pomelo TSSC detection were 0.8053 and 0.8888, respectively. Both reference and dark de-noising are important for pomelo internal quality detection and should be calibrated frequently to compensate for time drift. This study found that large sensor response translation noise can be reduced with an artificial horizontal shift. Data supplementation is efficient for improving the adaption of the detection model for batch differences in pomelo samples. Using this optimized de-noising model to compensate for time drift, sensor response translation, and batch differences, the developed detection method is capable of satisfying the requirements of the industry (TSSC detection R was equal or larger than 0.9, RMSE was less than 1). These results indicate that full-transmission VIS/NIR spectroscopy can be exploited to realize the nondestructive detection of pomelo TSSC on an industrial scale, and that the methodologies used in this study can be immediately implemented in real-world production.
柚子的风味变化很大，不剥开果实很难确定。柚子风味的品质很大程度上取决于果实中的总可溶性固形物含量（TSSC），因此在柚子的工业分级中，商业上需要一种快速且无损的TSSC检测方法。由于柚子个头大且中果皮厚，以无损方式确定柚子果实的内部品质很困难，而且其他水果内部品质检测的常用方法通常产生的噪声会使检测精度进一步复杂化。因此，本研究的目的是确定准确检测柚子TSSC的最佳方法，并找到一个去噪模型，以减少噪声对最佳方法结果的影响。在开发了全透射可见/近红外（VIS/NIR）光谱采样方法后，验证实验结果表明，最佳的柚子TSSC检测模型是Savitzky Golay+标准正态变量变换+竞争性自适应重加权采样+偏最小二乘回归。柚子TSSC检测校准集的R和RMSE分别为0.8097和0.8508，柚子TSSC检测验证集的R和RMSE分别为0.8053和0.8888。参考去噪和暗去噪对柚子内部品质检测都很重要，应经常校准以补偿时间漂移。本研究发现，通过人工水平移动可以减少较大的传感器响应平移噪声。数据补充对于提高检测模型对柚子样品批次差异的适应性很有效。使用这种优化的去噪模型来补偿时间漂移、传感器响应平移和批次差异，所开发的检测方法能够满足行业要求（TSSC检测R等于或大于0.9，RMSE小于1）。这些结果表明，全透射VIS/NIR光谱可用于在工业规模上实现柚子TSSC的无损检测，并且本研究中使用的方法可以立即应用于实际生产。
