To date, different approaches have been applied to measure the internal parameters of mangoes by destructive and non-destructive techniques. Recently, real-time evaluation of the internal parameters has become important assessment for fruits in high demand. This research aims to develop an online prototype system to measure the total soluble solids (TSS) in mangoes using a fiber optic diode array Visible-Near Infrared (Vis-NIR) spectrometer on a conveyor belt. Spectra were acquired in a wavelength range from 400–1000 nm. The diffuse reflectance spectra of mangoes were subjected to several preprocessing techniques such as moving average smoothing (MAS), standard normal variate (SNV), multiplicative scatter correction (MSC), baseline offset and normalization before model development. The spectral information and corresponding TSS values were used to establish a linear relationship by partial least squares (PLS) regression. Spectra in three wavelength ranges of 400–1000 nm, 600–1000 nm, and 700–1000 nm were used for the model development. Baseline offset combined with MAS showed effective transformation of spectra at a wavelength of 600–1000 nm. The optimum model was obtained by an external validation technique with a correlation coefficient of calibration set and a prediction set of 0.80 and 0.74, respectively. The root mean square error of the calibration (RMSEC), root mean square error of prediction (RMSEP) and bias were 0.690%, 0.765%, and 0.061%, respectively. The statistical results from PLS regression indicated the feasibility of using the online conveyor system for grading the fruit according to the TSS.

迄今为止，已经通过破坏性和非破坏性技术采用了不同的方法来测量芒果的内部参数。最近，内部参数的实时评估已成为对高需求水果的重要评估。这项研究的目的是开发一个在线原型系统，使用传送带上的光纤二极管阵列可见近红外（Vis-NIR）光谱仪测量芒果中的总可​​溶性固形物（TSS）。在400-1000 nm的波长范围内获得光谱。在开发模型之前，对芒果的漫反射光谱进行了多种预处理技术，例如移动平均平滑（MAS），标准正态变量（SNV），乘法散射校正（MSC），基线偏移和归一化。光谱信息和相应的TSS值用于通过偏最小二乘（PLS）回归建立线性关系。模型开发使用了400-1000 nm，600-1000 nm和700-1000 nm三个波长范围内的光谱。基线偏移量与MAS结合显示了600-1000 nm波长处光谱的有效转换。通过外部验证技术获得最佳模型，校正集的相关系数和预测集的相关系数分别为0.80和0.74。校准的均方根误差（RMSEC），预测的均方根误差（RMSEP）和偏差分别为0.690％，0.765％和0.061％。PLS回归的统计结果表明，根据TSS，使用在线输送机系统对水果进行分级的可行性。