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

Abstract: 【Objective】The city of Baise, located in Guangxi, China, exhibits a subtropical monsoon climate. The distinctive flavor of mangoes in this city is attributed to the unique combination of both climatic conditions and geographical environment. Baise's mango is characterized as a small core, high nutritional value and low fiber content, making it highly favored by consumers. Sugar content is an important 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 simple, 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 bandranges 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 hyperspectral 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 original 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 spectral 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 select 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 wavelengths. 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 wavelengths 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 varieties; nevertheless, a strong correlation existed within the red light range (600-700 nm) for all varieties. It was found that precise determination of characteristic wavelengths corresponding to chemical information 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 levels. The regression analysis revealed an ideal correlation between measured and predicted values, represented by the equation y=0.851 5x+12.208 (R2 =0.880 6). This relationship was further supported by a slope of 0.851 5, an intercept of 12.208, and RMSECV=0.636 6. 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 setshowed 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 successfully 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 demonstrated 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.