Machine learning driven benchtop Vis/NIR spectroscopy for online detection of hybrid citrus quality

The aim of this study was to explore application of visible and near-infrared (Vis/NIR) spectroscopy combined with machine learning models for SSC and TA prediction of hybrid citrus. The Vis/NIR spectra of samples including navel-region, equator-region and multi-region combination spectra in navel-region and equator-region were collected using a benchtop equipment. The performance of SSC and TA prediction models with different region spectra, including partial least squares (PLS), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM) and multilayer feedforward neural network (MFNN), was assessed. The accuracy of SSC and TA prediction models with multi-region combination (raw) spectra was better compared to navel-region and equator-region, suggesting that multi-region combination spectra collection method was more suitable. Subsequently, the spectral pre-processing, including Savitzky-Golay smoothing (SGS), maximum normalization (MN), multiplicative scatter correction (MSC), linear baseline correction (LBC) and first derivative (1stD), were performed. The performance of SSC and TA prediction models with different pre-processing spectra was further compared. The PLS with SGS spectra (SGS-PLS) and MFNN with raw spectra (Raw-MFNN) exhibited superior validation effects for SSC and TA prediction, respectively. In a subsequent prediction in new samples, SGS-PLS achieved an R_P² of 0.875, an RMSEP of 0.572% and a MAEP of 0.469% for SSC prediction, and Raw-MFNN achieved an R_P² of 0.800, an RMSEP of 0.0322% and a MAEP of 0.0249% for TA prediction, indicating excellent generalization ability. These results indicate the great potential of benchtop Vis/NIR spectroscopy for online detection of hybrid citrus quality at mass-scale level.