Sweetness Detection and Grading of Peaches and Nectarines by Combining Short- and Long-Wave Fourier-Transform Near-Infrared Spectroscopy

Abstract

Sweetness is one of the most important quality traits for fruit which directly affects consumers’ acceptance. The aim of this study was to detect and grade sweetness levels of intact peaches and nectarines by combining short-wave and long-wave Fourier-transform near-infrared spectroscopy (FT-NIRS). FT-NIR spectra of 720 samples (360 peaches and 360 nectarines) were collected using a system operated in range from 15,700 to 4000 cm⁻¹. Soluble solids contents (SSC) were measured as indicators and samples were individually grouped into three sweetness levels based on predetermined thresholds. Partial least squares regression (PLSR) and principal component regression (PCR) were compared, and results showed that PLSR models developed using raw full spectra performed best with correlation coefficient (R_p) of 0.7747 and 0.8473, and root mean square error (RMSEP) of 0.6915% and 0.7228% in prediction. Furthermore, effective wavelengths were individually selected using regression coefficients (RC) and competitive adaptive reweighted sampling (CARS) to simplify the PLSR models. Through comparison, RC-PLSR resulted in comparable performance with original models showing R_p = 0.7716 and 0.8157, and RMSEP = 0.6943% and 0.7945%. To grade different sweetness levels, two-dimensional correlation spectroscopy (2D-COS) was innovatively conducted, and autopeaks characterizing the differences among different groups were retained. Partial least square discriminant analysis (PLS-DA) classification models based on autopeaks achieved a total correct classification rate (CCR) of 66.7% and 86.6% for peaches and nectarines, respectively. The overall results indicated the combination of short-wave and long-wave FT-NIRS was potentially useful in detecting and grading sweetness for these fruit varieties.

Keywords:

• Fourier-transform near-infrared spectroscopy
• nectarine
• NIRS
• peach
• soluble solids content
• sweetness levels

Disclosure statement

No potential conflicts of interest are reported by the authors.

Additional information

Funding

This work was supported by the Scientific Research Foundation for Advanced Talents of Nanjing Forestry University by grant number 163040114.