基于近红外高光谱技术的杧果可溶性固形物含量无损检测

Fruit soluble solids content non-destructive detection based on visible/near infrared hyperspectral imaging in mango

【目的】近红外高光谱成像技术(NIR-HSI)在水果内部品质的无损检测方面具有快速、准确和无损的特点。旨在利用NIR-HSI技术分析不同品种杧果的可溶性固形物含量,并探讨400~1000nm波段范围内的光谱差异和可溶性固形物含量的响应。【方法】选择贵妃杧果和台农1号杧果作为研究对象,使用NIR-HSI技术获取杧果样本的光谱数据。采用CARS-PLS模型分析可溶性固形物含量与各波段光谱反射率的相关系数。为了验证模型的性能,计算了建模R^(2)、斜率Slope、截距和RMSE等指标。【结果】得到CARS-PLS模型的性能指标:建模R^(2)为0.8806,斜率为0.8515,截距为12.208,RMSE为0.6366。这些指标表明该模型具有较高的建模拟合度和预测精度。【结论】应用NIR-HSI技术对杧果可溶性固形物含量进行检测具有可行性。为进一步研究不同水果可溶性固形物含量的高精度模型奠定了基础。通过NIR-HSI技术的应用,可以提供一种非破坏性且高效准确的方法,用于水果品质评估和检测。这对农产品质量控制和市场营销具有重要的意义。

【Objective】The city of Baise,located in Guangxi,China,exhibits a subtropical monsoon cli-mate.The distinctive flavor of mangoes in this city is attributed to the unique combination of both cli-matic conditions and geographical environment.Baise's mango is characterized as a small core,high nu-tritional value and low fiber content,making it highly favored by consumers.Sugar content is an impor-tant indicator of the intrinsic quality of mangoes.With the increasing demand for mango grading and deep processing due to the improvement of people’s living standards,it is imperative to develop a sim-ple,rapid and non-destructive technique for detecting mango brix content.However,most researchers have focused on developing detection models for single species or classes of fruits using spectrometers with low stability and weak universality that hinder the industrialization of scientific research outcomes.Therefore,this study aimed to explore the differences in brix spectra and characteristic response band ranges among different types of mangoes using NIR-HSI technology.The ultimate goal was to establish a high-precision detection model for sugar content in various fruits with Guifei mango and Tainong No.1 mango serving as research objects.【Methods】The hyperspectral image data were acquired using a hy-perspectral imaging system.A total of 327 bands of hyperspectral images were obtained in the spectral range between 400-1000 nm for this experiment.The digital refractometer that we used was a portable digital refractometer PAL-1 from ATAGO,Japan.Measurements were taken three times independently,and the average value was calculated as the reference value for soluble solids in mango samples.After opening the original spectral image with ENVI software and extracting the original spectral data within a pixel square 10×10,the average spectral data of each region were manually selected and extracted.Subsequently,MATLAB R2018b software was employed to perform spectral data modeling and origi-nal segmentation of the image data.The multiple scattering correction(MSC)algorithm was chosen to effectively reduce random noise in the spectral data,with its noise reduction effect being influenced by the number of smoothing points utilized.Therefore,MSC preprocessing was applied to process the spec-tral data accordingly.To model different types of mango brix values along with their corresponding spectral reflectance as training data,we employed the KS algorithm.The remaining brix values and their corresponding spectral reflectance were treated as test data.The PLS model can be utilized to se-lect a smaller set of new variables that replaced a larger set without losing crucial spectral information.This addressed challenges posed by overlapping bands in spectroscopy analysis.【Results】The analysis of the spectral curves of different mango varieties showed that there were consistent overall trends among them.Notably,absorption peaks occurred at approximately 509,680,857 and 963 nm wave-lengths.In the red light region(680-750 nm),reflectance showed a distinct increasing trend with a steep slope formation.Thus,the characteristic wavebands for mango pulp can be identified as the range of 680-750 nm and specific bands at 509,550,680,857 and 963 nm.Within the range of 500-750 nm,Tainong No.1 mango exhibited significantly higher spectral reflectance compared to Guifei mango.Moreover,both fruits displayed steep slope formations in their spectral curves when sugar levels were similar;however,these slopes occurred at different positions.Specifically,Tainong No.1 mango's steep slope was observed around wavelengths of 500-640 nm while Guifei mango’s occurred around wave-lengths of 680-750 nm.Both varieties exhibited absorption peaks near wavelengths of approximately 680 and 857 nm,while similar trends were displayed in spectral reflectance within the range of 750-1000 nm.The response of spectral reflectance to sugar content varied widely among different mango va-rieties;nevertheless,a strong correlation existed within the red light range(600-700 nm)for all variet-ies.It was found that precise determination of characteristic wavelengths corresponding to chemical in-formation in mangos remained challenging,which may impact model accuracy.Therefore,this issue needs to be addressed in future studies to enhance accurate prediction models for determining mango saccharinity.Combined with the spectral reflectance data of different mango varieties,we can analyze the effect of their respective band ranges on sugar content.The peak response was observed at about 670 nm with a correlation coefficient of 0.837,indicating the highest spectral sensitivity.Notably,the CARS-PLS prediction model exhibited superior accuracy and reliability in predicting mango brix lev-els.The regression analysis revealed an ideal correlation between measured and predicted values,repre-sented by the equation y=0.8515x+12.208(R2=0.8806).This relationship was further supported by a slope of 0.8515,an intercept of 12.208,and RMSECV=0.6366.The PLS model constructed using wavelengths with high correlation coefficients between brix and spectral reflectance in each band gave better results in predicting mango brix.【Conclusion】Both the calibration set and the prediction set showed that the predicted values were very close to the corresponding actual values.The results showed that it was feasible to apply hyperspectral imaging technology to detect mango brix.This study success-fully employed NIR-HSI technology to analyze the differences in spectral and characteristic response bands of mangoes with varying sugar contents.The developed high-precision detection model demon-strated promising results in predicting mango brix.These findings have validated the feasibility of employing hyperspectral imaging technology for mango brix detection,with great potential applications in mango grading and processing.Further research is warranted to enhance accurate saccharinity prediction by precisely identifying characteristic wavelengths associated with chemical information in mangoes.