Hyperspectral estimation of the soluble solid content of intact netted melons decomposed by continuous wavelet transform

Abstract

Netted melons are welcomed for their soft and sweet pulp and strong aroma during the best-tasting period. The best-tasting period was highly correlated with its soluble solid content (SSC). However, the SSC of the intact melon was difficult to determine due to the low relationship between the hardness, color, or appearance of fruit peel and its SSC. Consequently, a rapid, accurate, and non-destructive method to determine the SSC of netted melons was the key to determining the best-tasting period. A hyperspectral model was constructed to estimate the SSC of intact netted melons. The combination of continuous wavelet transform and partial least squares or random forest algorithm was employed to improve the estimation accuracy of the hyperspectral model. Specifically, the hyperspectra of the diffuse reflection and SSC of 261 fruit samples were collected. The sensitivity band was screened based on the correlation analysis and continuous wavelet transform decomposition. The correlation coefficient and RMSE of the random forest regression model decomposed by the continuous wavelet transform were 0.72 and 0.98%, respectively. The decomposition of the continuous wavelet transform improved the correlation coefficient by 5 and 1.178 times at 754 and 880 nm, respectively. The random forest regression model enhanced the determination coefficient by at least 56.5% than the partial least squares regression model, and the continuous wavelet transform decomposition further enhanced the determination coefficient of the random forest regression model by 4.34%. Meanwhile, the RMSE of the random forest regression model was reduced. Therefore, the decomposition of the continuous wavelet transform improved the stability and prediction ability of the random forest regression model.

Study on Rapid Non-Destructive Detection Method of Corn Freshness Based on Hyperspectral Imaging Technology

“…Competitive adaptive re-weighted sampling (CARS) and the sequential projection algorithm (SPA) were used to extract the characteristic wavelengths associated with the fatty acid values of corn [37][38][39]. Two methods, a BP neural network (BPNN) and random forest (RF), were used to classify and regressively analyze the spectral data and to build a model to predict the fatty acid values of corn [40][41][42].…”

Prediction of Solid Soluble Content of Green Plum Based on Improved CatBoost

“…The hyperspectral technology was used to predict SSC in netted melons by continuous wavelet transformation. The correlation coefficient and RMSE of the random forest regression model decomposed by the continuous wavelet transform were 0.72 and 0.98%, respectively [13].…”