Short wave-near infrared spectroscopy for predicting soluble solid content in intact mango with variable selection algorithms and chemometric model

Mango sweetness is one of the most prominent internal quality constituents that attract consumer's attention. The current common methods used to determine the sweetness of mango have significant disadvantages in that labor-intensive, time-consuming, and damaging techniques. This research aims to predict sweetness in mangoes using short wave-near infrared (SW-NIR) spectroscopy in the ranges 900–1650 nm, along with variable selection algorithms and chemometric model. A total of 120 mango samples were used to collect the spectra using a fibre module SW-NIR spectrometer. The partial least squares (PLS) regression with several spectral preprocessing methods was employed to develop the calibration model, and the best preprocessing technique was selected. The Savitzky–Golay second-derivative preprocessing technique performed better among the other preprocessing techniques with a correlation coefficient of prediction (r_pred) of 0.74 and standard error of prediction (SEP) is 0.78 %Brix. After that, two variable selection techniques were used to select effective wavelength variables, including regression coefficient and successive projections algorithm (SPA). For SSC prediction in the range 900–1650 nm, the SPA-PLS model obtained a r_pred of 0.78 and SEP of 0.67 %Brix. The current study unequivocally shows that the proposed SW-NIR spectroscopy coupled with a suitable chemometrics method can evaluate mango sweetness nondestructively.