Rapid assessment of calcium carbide ripened bananas using machine-learning assisted laser Raman spectroscopy.

Abstract

Fruit ripening is usually a natural process in which fruits undergo various chemical and physical changes before they become palatable. New artificial ways of fruit ripening have been developed as a result of recent breakthroughs in agricultural technology mainly to meet market demands and to deal with the logistics of storage and transportation. However, this practice has become a concern because of the human health risks resulting from the uncontrolled use of ripening agents which contain toxic elements. For instance industrial grade calcium carbide has impurities of arsenic and phosphorus as well as other heavy metals. High intake of these elements is known to cause neurological disorders such has cerebral edema and memory loss as well as carcinogenic disorders like cancer of the colon, lungs and peptic ulcers. Hardly is there a method capable of rapidly and non-invasively assessing artificial ripeners in fruits (ARF) with reliability and accuracy. The wet chemical techniques conventionally used such as the various forms of chromatography are time consuming, destructive, costly and involve laborious sample preparations. This work aims at developing a rapid and non-invasive technique for assessing calcium carbide ripened bananas using machine-learning (ML) assisted laser Raman spectroscopy (LRS). In this study, Raman spectra was recorded from naturally and carbide ripened banana samples using a 785 nm laser for excitation. The bananas were ripened using calcium carbide with concentrations ranging from 0.240 g/L to 2.0 g/L. Exploratory analysis using PCA revealed that clustering of the carbide ripened samples was due to the presence of sulfur, acetylene, calcium hydroxide and phosphine impurities contained in CaC2. These molecules have Raman bands centered at 480 cm-1 (S-S bond stretching), 612 cm-1 (C-H asymmetric bending), 780 cm-1 (O-H bending) and 979 cm-1 (P-H stretching) respectively. Classification and quantification of CaC2 concentrations used in ripening was achieved using the following ML algorithms: support vector machine, artificial neural networks and random forest. High correct classification accuracies were realized (>85 %) in the ML classification models. Furthermore, the performance of the regression models showed good performance as indicated by high R2 values (>0.95) and the low RMSEP values (<0.34g/L) when predicting test data sets. Banana samples collected from local markets around Nairobi were found to have been ripened by CaC2 (up to 1.30 g/L) using the optimized LRS conditions and ML models developed in this work. Therefore, ML-assisted LRS allows for rapid and direct assessment of artificial ripeners in fruits. The findings of this study will aid in the development of spectral libraries for use in food safety analysis procedures involving fruits.