柚子果皮厚, 果皮与果肉属于两种不同的介质, 对光的折射、 吸收程度存在差异, 针对建立水果可溶性固形物含量(SSC)检测模型时, 光谱采集量与目标不匹配, 导致模型精度差的问题, 以上饶马家柚为研究对象, 自主搭建可调实验平台, 采集并分析柚子整果的光能量衰减规律, 寻找柚子厚度与透光性的关系, 探索果皮厚度、 光透射深度对柚子SSC检测精度的影响。 首先将透射光源放置在柚子赤道圈的正上方, 统计柚子赤道圈不同区域接收到的光谱强度, 绘制光谱强度分布图, 结果显示, 距离光源发射点越远, 光谱强度越低, 入射点由远及近的位置接收的光强分别占33.40%、 2.90%、 0.50%、 0.40%、 0.20%, 柚子皮对光的吸收较为明显, 散射出的光所占比重较少; 采用切片法, 记录剩余厚度与对应的光谱强度值, 绘制光谱强度的变化规律曲线, 随着剩余厚度逐渐减少, 光谱强度逐渐增加, 在32.90 mm的位置, 光谱强度发生了巨大的变化, 果实厚度高于32.92 mm时, 果实接收的光谱强度普遍较低, 当果实低于32.92 mm时, 光谱强度呈跳跃式增加。 采集果肉、 整果、 果皮光谱, 采用偏最小二乘法(PLS)建立SSC预测模型, 去皮后的果肉模型相关性最高。 采集柚子果肉、 果皮+果肉厚度为40、 30、 20和10 mm时的光谱, 建立不同厚度的SSC预测模型, 果肉厚度为20、 40、 60和80 mm时, 预测集相关系数分别为0.91、 0.89、 0.87和0.86, 果肉在透射深度为20 mm时, 水果SSC预测模型精度最佳。 果皮+果肉的光谱透射深度为20、 40、 60和80 mm, 预测集相关系数分别为0.78、 0.86、 0.93和0.84, 果皮+果肉的透射深度为60 mm时, 有最好的预测效果。 研究结果表明, 果皮和果肉内部组织成分的差异, 会影响SSC预测的结果, 但是调整可见/近红外光在水果内部的传输距离, 可以优化模型精度, 研究揭示了可见/近红外光在水果组织中的漫透射传输特性, 可为厚皮果的品质在线分选装置研发提供实验依据。

Grapefruit peel is thick. The peel and pulp belong to two different media, and the refractive index and absorption of light from the peel and pulp are different. Modeling of grapefruit soluble solids content without removing the interference of fruit peel, can lead to poor model accuracy. To address the problem of poor model accuracy due to the mismatch between spectral acquisition and target when building a fruit quality detection model. To address the problem of poor model accuracy due to the mismatch between spectral acquisition and target when establishing fruit quality detection models, this study takes ShangraoMajiya grapefruit as the experimental object, builds an adjustable experimental platform independently, acquires and analyzes the light energy attenuation pattern of whole grapefruit, searches for the relationship between grapefruit thickness and light transmission, and explores the effects of peel thickness and light transmission depth on grapefruit SSC detection accuracy. Firstly, the transmitted light source was placed directly above the equatorial circle of the grapefruit, and the spectral intensity received by different regions of the equatorial grapefruit circle was counted. The spectral intensity distribution was plotted, and the results showed that the further away from the light source emission point, the lower the spectral intensity. The light intensity received at the incident point from far and near positions accounted for 33.40%, 2.90%, 0.50%, 0.40%, 0.20%. The absorption of light by the grapefruit peel was more obvious, and the scattered light energy accounted for a smaller proportion. Secondly, the slicing method was used to record the remaining thickness and the corresponding spectral intensity, and to draw the curve of the changing pattern of spectral intensity.The smaller the remaining thickness, the greater the spectral intensity when the thickness was 32.90 mm, the spectral intensity changed dramatically, when the fruit thickness was higher than 32.92 mm, the photon intensity received by the fruit was generally lower when the fruit was lower than 32.92 mm, the spectral intensity undertook a jump increase. Then the flesh, whole fruit and peel spectra were collected, and the SSC prediction model was developed using partial least squares, and the best prediction was obtained for the peeled flesh. Finally, the spectra were collected when the thickness of grapefruit flesh, peel + flesh was 40, 30, 20 and 10 mm, and the SSC prediction models with different thicknesses were established. The correlation coefficients of the prediction sets were 0.91, 0.89, 0.87 and 0.86 when the thickness of flesh was 20, 40, 60 and 80 mm respectively. The SSC prediction model had the highest accuracy when the flesh was at a transmission depth of 20 mm. The spectral transmission depth of peel+pulp was 20, 40, 60 and 80 mm, and the prediction set correlation coefficients were 0.78, 0.86, 0.93 and 0.84, respectively, with the best prediction at a transmission depth of 60 mm for peel+pulp.The results show that the difference in tissue composition inside the fruit skin and pulp affects the results of SSC prediction, but changing the transmission distance of visible/NIR inside the fruit can also optimize the model accuracy. This study reveals the diffuse transmission characteristics of visible/NIR in fruit tissue, which can provide a realistic basis for developing online sorting devices for the quality of thick-skinned fruits.