油茶果自然霉变程度的可见/近红外与中短波近红外光谱检测

分析利用可见/近红外光谱（400～1 000 nm）与中短波近红外光谱（900～1 700 nm）对不同自然霉变程度油茶果检测判别的可行性，实验同时采集不同霉变程度油茶果赤道阴面、阳面和接合面三点的两波段光谱，样品平均光谱的主成分分析（principal component analysis,PCA）发现不同霉变程度样品同组内具有一定聚类效果且PC1和PC2对于判别不同组间样品有效，全光谱偏最小二乘判别分析模型结果显示原始光谱已具有足够信息，建立的模型性能比预处理后全光谱更优。进一步进行特征波长选取，发现相比于PC载荷，连续投影法在两光谱范围选取波长建立的简化模型均为最优，预测集判别准确率与Kappa系数均为84.4%与0.766 7。结合预测集混淆矩阵发现，两光谱范围最优简化模型预测不同霉变组样品特异度相当，均在0.84以上，但900～1 700 nm中短波近红外光谱对于中等霉变程度的判别灵敏度（0.72）略高。本研究表明近红外光谱技术可用于油茶果的自然霉变程度检测，可见/近红外与中短波近红外光谱能力相当，考虑到仪器成本问题，可见/近红外光谱具有更好的实时检测应用前景。

Detection of the Degree of Natural Mildew of Camellia oleifera Fruit Using Visible/Near Infrared,Mid- and Short-Wave Near Infrared Spectroscopy

This study explored the feasibility of applying visible and near-infrared (400?1 000 nm), and mid- and short-wave near infrared (900?1 700 nm) spectroscopy for detecting Camellia oleifera fruit with different degrees of natural mildew. The near infrared spectra of the equatorial shady and sunny sides, and the junction surfaces of samples with different degrees of mildew were collected in two wavelength bands. The average spectra were analyzed by principal component analysis (PCA), revealing that samples with different mildew degrees could be clustered into different groups, and the first and second principal components (PC1 and PC2) were effective in distinguishing the samples in each category. The full-spectrum partial least squares-discriminant analysis (PLS-DA) model based on the original spectra performed better than of its counterpart based on the preprocessed spectra. In the selection of characteristic wavelengths, successive projections algorithm (SPA) was found to be superior to PC loadings in establishing the simplified models for both spectral ranges. The correct classification rate and kappa coefficient were 84.4% and 0.766 7 for the prediction set, respectively. Based on the observation of confusion matrices for the prediction set, the specificities of the two optimal simplified models for the prediction of each degree of mildew were equivalent to each other and above 0.84. However, mid- and short-wave near infrared spectra in the wavelength range of 900–1 700 nm provided slightly higher sensitivity (0.72) in classifying samples with moderate degree of mildew. Our study showed that near-infrared spectroscopy could be used to detect the degree of natural mildew of C. oleifera fruit, and visible and near-infrared spectroscopy showed comparable results to mid- and short-wave near infrared spectroscopy. Considering its lower cost, visible and near-infrared spectroscopy has a better application prospect for real-time detection.