Ensuring high-quality agricultural products has become important in agriculture since society’s standard of living has risen. Meanwhile, Moringa oleifera L. leaf extract (MLE) has been used as a plant biostimulant to improve product quality. The effectiveness of MLE is associated with its beneficial components, consisting of nutrients, phytohormones, secondary metabolites, amino acids, and bioactive compounds. Previous studies have been carried out to find the effects of MLE application on the quality of different crops, including basil, kale, spinach, maize, radish, brinjal, pepper, tomato, grape, strawberry, and more. The results are generally positive concerning physical, nutritional, and chemical qualities. This review comprises recent findings regarding MLE application as a plant biostimulant to increase quality attributes, with its underlying mechanism.

Guava fruit is one of the most popular horticulture products because it has various health benefits. Once fruit is harvested, the fruit is still carrying out the respiration process during storage which results in changes in physical and chemical properties. One of chemical properties that is concerned by consumers is the total soluble solids, which explain the rough sugar content. The study examined the use of handheld near-infrared instruments to predict the total soluble solids of guava fruit at various storage periods rapidly and non-destructively. The research method used in this study was multivariate data analysis. Spectra pre-treatments were applied to correct the spectra and increase the accuracy of prediction. Calibration model was done by partial least squares regression (PLSR) and principal component regression (PCR). The results showed that the use of handheld near-infrared instrument was able to predict the total soluble solids of guava fruit with high accuracy. The best calibration model was produced by PLSR calibration method integrated with orthogonal signal correction (OSC) spectra pre-processing technique.

Latest studies on Vis/NIR research mostly focused on particular products. Developing a model for a specific product is costly and laborious. This study utilized visible/near-infrared (Vis/NIR) spectroscopy to evaluate the quality attributes of six products of the Cucurbitaceae family, with a single estimation model, rather than individually. The study made use of six intact products, zucchini, bitter gourd, ridge gourd, melon, chayote, and cucumber. Subsequently, the multi-product models for soluble solids content (SSC) and water content were created using partial least squares regression (PLSR) method. The PLSR modeling produced satisfactory results, the coefficient of determination in calibration set (R2c) was discovered to be 0.95 and 0.92, while the root mean squares error of calibration (RMSEC) was found to be 0.41 and 0.61, for SSC and water content, respectively. These models were able to accurately predict the unknown samples with coefficient of determination in prediction set (R2p) of 0.96 and 0.92, as well as root mean squares error of prediction (RMSEP) of 0.32 and 0.58, while the ratio of prediction to deviation (RPD) was found to be 5.68 and 3.69 for SSC and water content, respectively. This shows Vis/NIR spectroscopy was able to quantify the SSC and water content of six products of Cucurbitaceae family, using a single model.

In recent decades, the development of non-destructive measurement methods for agricultural commodities has gained a lot of attention among scientists, but these techniques have different levels of accuracy for each instrument used. Therefore, this study aimed to compare the prediction accuracy of natural pigments, such as Total Carotenoid Content (TCC) and Total Flavonoid Content (TFC) using a color spectrophotometer and Visible/Near-Infrared (Vis/NIR) spectroscopy (381–1065 nm). The effect of ethephon concentration on the spectral characteristics and the accuracy of predicting pigments was studied. The samples used include cucumber fruit, which consisted of the 'Mars', 'Vanesa', and 'Roberto’ varieties. During the planting of the fruit, ethephon was applied at different concentrations of 0 ppm, 150 ppm, and 300 ppm. The results showed that the best accuracy for predicting TCC was obtained through a color spectrophotometer (R_cal = 0.89, R_pred = 0.90, RPD = 2.44), while the best prediction for TFC was the Vis/NIR spectroscopy (R_cal = 0.86, R_pred = 0.83, RPD = 1.78). Furthermore, the ethephon affects the spectral characteristics of cucumber fruit. Ethephon concentration of 150 ppm produced the highest accuracy value compared to others. This study proved that the use of non-destructive measurement methods with a color spectrophotometer and Vis/NIR spectroscopy has good performance in predicting TCC and TFC. The techniques are also easy to use, do not require chemicals, and have high accuracy.

This study aimed to obtain the best calibration model from various spectra pre-treatment methods to assess sapodilla fruit firmness using vis-nir spectroscopy. Before the spectra data measurement, samples were treated with storage of 0, 5 and 10 days at room temperature. Spectra data measurement was carried out using the NirVana AG410 visible and near infrared spectrometer from 312 to 1050 nm with interval of 3 nm. RAW spectra were pre-treated using the multiplicative scatter correction (MSC), standard normal variate (SNV), and Savitzky-Golay first derivative (dg1) with 9 points of smoothing. The calibration model was developed using PLS (partial least squares) method. Validation was done by K fold cross validation method. The results showed the MSC and SNV spectra were able to eliminate noises of RAW spectra, whereas in the dg1 spectra, noises were still visible. The best model was acquired by SNV spectra with R² (coefficient of determination) of calibration and validation of 0.882 and 0.870, root mean square error of calibration (RMSEC) and root mean square error of cross validation (RMSECV) values of 2.92 and 3.08, and the ratio of performance to deviation (RPD) of 2.76. The result indicated the spectra pre-treatments were able to improve the accuracy of calibration model on assessment of sapodilla fruit firmness.

Fruits are one of the sources of nutrition needed for health. Fruit quality is generally assessed by physical and chemical properties. Measurement of fruit internal quality is usually done by destructive techniques. Ultraviolet, visible and near-infrared (UV-Vis-NIR) spec-troscopy is a non-destructive technique to measure fruit quality. This technique can rapidly measure the fruit quality, the measured fruit still remains intact, and can be marketed. Besides, UV-Vis-NIR spectrosco-py can also be used to classify fruits. The study aimed to classify var-ious types of fruits using UV-Vis-NIR spectroscopy with wavelengths of 300-1041 nm and Principal Component Analysis (PCA). First de-rivative savitzky-golay with 9 smoothing points (dg1) and multiplica-tive scatter correction (MSC) were applied to correct the spectra. The results showed that the use of uv-vis-nir spectroscopy and PCA com-bined with spectra pre-treatment of the MSC method were able to clas-sify various types of fruits with 100% success rate in all fruit samples including sapodilla, ridge gourd, mango, guava, apple and zucchini.