Determination of translucent content in mangosteen by means of near infrared transmittance

Translucent flesh disorder is undesirable in mangosteen meant for export. However, mangosteens are judged as translucent when the translucent flesh is visible on the pulp surface regardless of the quantity of the internal translucent flesh which may result in some mangosteen assessed as normal having the same amount of translucent flesh content as a mangosteen judged as translucent. The critical amount of translucent flesh to be visible on the pulp surface needs to be determined for assessment purposes. A non-destructive technique to measure the translucent content is a practical tool as the first step towards the establishment of the critical value.A non-destructive model was developed to estimate the translucent content in mangosteens using near infrared transmittance. The translucent area of the flesh section on the fruit surface was used to indicate the translucent content. The effects of the orientation of the fruit and also of the light source to the relative position of the detector as well as the effect of the measurement position of the fruit on the predictive performance were examined. The results showed that the best partial least squares model was achieved with spectra acquired from the fruit position which revealed the largest flesh segment (prediction correlation coefficient was 0.86 and root mean square error of prediction was 7.58%). The horizontal stem-calyx fruit axis and a 135° angle from the light source relative to the detector were the optimal fruit orientation and configuration for measurement.     

Non-destructive classification of durian maturity of ‘Monthong’ cultivar by means of visible spectroscopy of the spine

Characteristics of the spine of ‘Monthong’ durian such as color of the tip have been known to be an indicator of fruit maturity. Visible spectroscopy of the spine of durian was investigated for classification of maturity. Partial least squares discriminant analysis was performed to model the classification. The model using absorbance spectra transformed by the standard normal variate achieved the best accuracy of classification (94.7%) into four maturity classes ranging from 113 to 134 days after anthesis. The classification was attributable to the absorbance of chlorophyll a, carotenoids and anthocyanins in the spine.     

Minimally-destructive evaluation of durian maturity based on electrical impedance measurement

Electrical impedance spectroscopy was investigated to model the dry matter content of durian using partial least squares regression. Measurements of the impedance were taken on the stem and the rind of durian samples at various stages of maturity based on the number of days after anthesis. Plots of the relationship between resistance and reactance, and the change in impedance and capacitance with respect to frequency in a range of 1–200 kHz were explored to determine the optimal frequencies associated with variation in the number of days after anthesis. The impedance parameters at frequencies of 1, 41 and 200 kHz were employed to model the dry matter content of the pulp. The reactance of the cross section of the stem and the capacitance of the rind were found to predominantly contribute to the prediction of the dry matter content. Selected impedance parameters using a stepwise regression could be used to classify durian samples into an immature class and mature class with less accuracy of 83.3%.     

Evaluation of pomelo maturity based on acoustic response and peel properties

This research investigated the maturity assessment of pomelo using acoustic properties obtained from an impact of fruit, optical properties of the peel and variables related to oil glands from peel images. Pomelo samples were harvested at 5.5, 6.0, 6.5 and 7.0 months after anthesis. All nondestructive variables were used to build qualitative models with partial least squares discriminant analysis. The classification model based on the nondestructive variables showed that fruits could be separated into immature, early‐mature and late‐mature groups with an accuracy of 96.7%. The important variables contributing to the classification were the impact response based on the second‐order resonant frequency and the difference of green colour between the oil gland and the peel.     

Maturity sorting index of dragon fruit: Hylocereus polyrhizus

The purpose of this research was to investigate maturity prediction of red flesh dragon fruit based on non-destructive measures. Specific weight, sphericity, color value L, a, b and light reflectance spectrum were linearly combined by partial least squares regression (PLSR) analysis. The PLSR models could predict days after fruit set, weight ratio and total soluble solids relatively well with standard deviation divided by standard error of prediction (RPD) of 2.86, 2.45 and 2.38, respectively. Date after fruit set, total soluble solids, total acid, ratio of total soluble solids and total acidity and weight ratio were transformed into a principal component 1 (PC1) by the principal component analysis and used to represent a single maturity index. The PLSR model with non-destructive parameters resulted in an improved performance in the prediction of the maturity index (PC1) with a RPD increase to 3.49. The model could be further simplified but retained a comparable accuracy by the application of a log (R680/R550) in place of the light reflectance spectrum.     

Non-destructive prediction of hardening pericarp disorder in intact mangosteen by near infrared transmittance spectroscopy

A non-destructive technique to predict a hardening pericarp disorder in intact mangosteen is proposed by using near infrared (NIR) transmittance spectroscopy in the wavelength range of 660-960 nm. The study found that the spectral features of normal pericarp mangosteen and hardening pericarp mangosteen were different. The averaged spectra and individual spectra of hardening pericarp mangosteen from a calibration set (N = 560) were used to develop classification models, using partial least squares discriminant analysis (PLS-DA). A model based on individual spectra obtained better classification. The overall accuracy of classification for a prediction set (N = 358) was 91%. Out of 179 samples of normal pericarp fruits, 167 were identified correctly, while 159 samples out of 179 samples with hard pericarp were predicted correctly. The results showed that NIR transmittance spectroscopy can be used to predict hard pericarp disorder in intact mangosteen fruit accurately.     

Combination of visible reflectance and acoustic response to improve non-destructive assessment of maturity and indirect prediction of internal quality of red-fleshed pomelo

Maturity and the internal quality of red-fleshed pomelo in terms of the sugar–acid ratio were studied by assessing whole fruit properties using acoustic response and local properties based on the visible reflectance of the fruit surface. Pomelo fruit samples were harvested at different maturity stages. Maturity classification and prediction models were built using discriminant analysis and partial least squares regression, respectively. Better classification was achieved by the model incorporating both surface visible reflectance and the resonant frequency compared with the model based on only surface visible reflectance. Similarly, the sugar–acid ratio was better predicted by the model using both acoustic response and surface visible reflectance. The accuracy of prediction was maintained when the data from two harvest seasons were combined to build the model for prediction.     

Near-infrared hyperspectral imaging for classification of mung bean seeds

Hard mung bean seeds pose a problem in the sprouting process as they develop mold and infect neighboring seeds. Near-infrared hyperspectral imaging combined with partial least squares discriminant analysis was applied to develop a classifying model to separate hard mung beans from normal ones. The orientation of the measured beans was found to affect the classification result. The optimal partial least squares discriminant analysis model based on all orientations resulted in a correlation coefficient (R) of 0.919 with a root mean squared error of prediction of 0.197. The non-germinative parts were mapped and were concentrated at one end of the bean. Finally, a germinability index was proposed according to the proportion of colored areas between the germinative and non-germinative parts from the hyperspectral imaging results.     

Non-destructive maturity classification of mango based on physical, mechanical and optical properties

Mango is a popular tropical fruit and right maturity at harvest is important for eating quality and shelf life. Therefore, harvesting needs skilled pickers otherwise fruit of varying levels of maturity will be collected. This research investigated maturity classification of mango fruits (cv. Nam Dokmai) using physical, mechanical and optical properties. Variation in size, sphericity, specific gravity, total soluble solids, total acidity, surface color, acoustic response (stiffness coefficient based on the resonant frequency and the weight of the fruit), impact response (peak acceleration/corresponding time) were followed during a number of days after fruit set from 77 to 115 days. On discriminant analysis, mangoes of three maturity classes based on days after fruit set could be separated using non-destructive parameters (95%). Cluster analysis was applied based on destructive parameters (total soluble solids and total acidity) and days after fruit set to pre-allocate each fruit into four different maturity classes. The 89.0% accuracy of classification into four levels of maturity could be achieved by simplified non-destructive model. The best applicable variables of physical, mechanical and optical properties were SG, stiffness coefficient and diffuse reflectance at 670 nm, respectively.     

Application of Near Infrared Spectroscopy for Indirect Evaluation of “Monthong” Durian Maturity

Durian contains thick rind which restricts light penetration into the pulp region. Indirect prediction of pulp dry matter as a reference of maturity was investigated using spectral information from the rind and stem. Partial least squares regression was performed to model variation in the pulp dry matter using the rind and stem absorbance. The rind model showed better performance in predicting the dry matter content than the stem model. However, the accuracy was relatively poor (correlation coefficient of prediction, r_p = 0.76 and root mean square error of prediction, RMSEP = 1.82%) compared with that of the reference pulp model (r_p = 0.83 and RMSEP = 1.61%). The rind model was superior to the stem model in the classification of durian samples into immature, early-mature, and mature classes based on the number of days after anthesis and the dry matter content. Effective wavelengths were chosen from the regression coefficients of the corresponding models and used to create a simplified classification model. A combination of both rind and stem spectral data at selected wavelengths provided the highest accuracy of classification (94.4%).