The use of visible and near infrared spectroscopy for evaluating passion fruit postharvest quality

Nondestructive Determination of Epicarp Hardness of Passion Fruit Using Near-Infrared Spectroscopy during Storage

The hardness of passion fruit is a critical feature to consider when determining maturity during post-harvest storage. The capacity of near-infrared diffuse reflectance spectroscopy (NIRS) for non-destructive detection of outer and inner hardness of passion fruit epicarp was investigated in this work. The passion fruits' spectra were obtained using a near-infrared spectrometer with a wavelength range of 10,000-4000 cm-1. The hardness of passion fruit's outer epicarp (F1) and inner epicarp (F2) was then measured using a texture analyzer. Moving average (MA) and mean-centering (MC) techniques were used to preprocess the collected spectral data. Competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), and uninformative variable elimination (UVE) were used to pick feature wavelengths. Grid-search-optimized random forest (Grids-RF) models and genetic-algorithm-optimized support vector regression (GA-SVR) models were created as part of the modeling process. After MC preprocessing and CARS selection, MC-CARS-Grids-RF model with 7 feature wavelengths had the greatest prediction ability for F1. The mean square error of prediction set (RMSEP) was 0.166 gN. Similarly, following MA preprocessing, the MA-Grids-RF model displayed the greatest predictive performance for F2, with an RMSEP of 0.101 gN. When compared to models produced using the original spectra, the R2P for models formed after preprocessing and wavelength selection improved. The findings showed that near-infrared spectroscopy may predict the hardness of passion fruit epicarp, which can be used to identify quality during post-harvest storage.

Non-Destructive Evaluation of the Physiochemical Properties of Milk Drink Flavored with Date Syrup Utilizing VIS-NIR Spectroscopy and ANN Analysis

A milk drink flavored with date syrup produced at a lab scale level was evaluated. The production process of date syrup involves a sequence of essential unit operations, commencing with the extraction, filtration, and concentration processes from two cultivars: Sukkary and Khlass. Date syrup was then mixed with cow's and camel's milk at four percentages to form a nutritious, natural, sweet, and energy-rich milk drink. The sensory, physical, and chemical characteristics of the milk drinks flavored with date syrup were examined. The objective of this work was to measure the physiochemical properties of date fruits and milk drinks flavored with date syrup, and then to evaluate the physical properties of milk drinks utilizing non-destructive visible-near-infrared spectra (VIS-NIR). The study assessed the characteristics of the milk drink enhanced with date syrup by employing VIS-NIR spectra and utilizing a partial least-square regression (PLSR) and artificial neural network (ANN) analysis. The VIS-NIR spectra proved to be highly effective in estimating the physiochemical attributes of the flavored milk drink. The ANN model outperformed the PLSR model in this context. RMSECV is considered a more reliable indicator of a model's future predictive performance compared to RMSEC, and the R2 value ranged between 0.946 and 0.989. Consequently, non-destructive VIS-NIR technology demonstrates significant promise for accurately predicting and contributing to the entire production process of the product's properties examined.

Flavonoids Accumulation in Fruit Peel and Expression Profiling of Related Genes in Purple (Passiflora edulis f. edulis) and Yellow (Passiflora edulis f. flavicarpa) Passion Fruits

Flavonoids play a key role as a secondary antioxidant defense system against different biotic and abiotic stresses, and also act as coloring compounds in various fruiting plants. In this study, fruit samples of purple (Passiflora edulis f. edulis) and yellow (Passiflora edulis f. flavicarpa) passion fruit were collected at five developmental stages (i.e., fruitlet, green, veraison, maturation, and ripening stage) from an orchard located at Nanping, Fujian, China. The contents of flavonoid, anthocyanin, proanthocyanin, and their metabolites were determined using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS), activities of key enzymes involved in flavonoid metabolism were measured, and expression profiling of related genes was done using quantitative real-time PCR (qRT-PCR). The results revealed that total flavonoids, anthocyanins, and procyanidins were found to be increased in the fruit peel of both cultivars with fruit maturity. Total flavonoids, anthocyanins, procyanidins, flavonoid metabolites (i.e., rutin, luteolin, and quercetin), and anthocyanin metabolites (i.e., cyanidin-3-O-glucoside chloride, peonidin-3-O-glucoside, and pelargonidin-3-O-glucoside) were found abundant in the peel of purple passion fruit, as compared to yellow passion fruit. Principle component analysis showed that the enzymes, i.e., C4H, 4CL, UFGT, and GST were maybe involved in the regulation of flavonoids metabolism in the peel of passion fruit cultivars. Meanwhile, PePAL4, Pe4CL2,3, PeCHS2, and PeGST7 may play an important role in flavonoid metabolism in fruit peel of the passion fruit. This study provides new insights for future elucidation of key mechanisms regulating flavonoids biosynthesis in passion fruit.

Effects of power ultrasound treatment on the shelf life of button mushrooms: Digital image processing and microbial counting can reveal the effects

Edible button mushroom (Agaricus bisporus) is the most common commercial-grade mushroom in the world. The shelf life of button mushrooms is limited to a range between two and four days because of enzymatic browning at medium ambient temperature if it is minimally processed. This study aimed to investigate the effects of power ultrasonics and its interaction with several treatments including H2O2 and O3 on increasing the storage quality of edible button mushroom by controlling enzymatic browning. A 100 W ultrasonic bath with a frequency between 20 and 35 kHz was used during the experiments. The storage quality was studied by examining the changes in color and microbial content over 12 days. The results obtained from the digital image processing and total microbial counting showed that the ultrasonic treatment for 6 min is an appropriate method for controlling the color preservation of mushrooms to improve their shelf life. The maximum changes in RGB band, HSV band, L*a*b* band, and microbial content of the mushroom samples under the ultrasonic treatment were equal to 7%, 12%, 10%, and 11%, respectively. Furthermore, having the color properties and microbial content of the samples, the artificial neural network (ANN) was capable of predicting their storage period with an MSE of 0.011.

Changes in the Content of Organic Acids and Expression Analysis of Citric Acid Accumulation-Related Genes during Fruit Development of Yellow (Passiflora edulis f. flavicarpa) and Purple (Passiflora edulis f. edulis) Passion Fruits

Organic acids are key components that determine the taste and flavor of fruits and play a vital role in maintaining fruit quality and nutritive value. In this study, the fruits of two cultivars of passion fruit Yellow (Passiflora edulis f. flavicarpa) and purple (Passiflora edulis f. edulis) were harvested at five different developmental stages (i.e., fruitlet, green, veraison, near-mature and mature stage) from an orchard located in subtropical region of Fujian Province, China. The contents of six organic acids were quantified using ultra-performance liquid chromatography (UPLC), activities of citric acid related enzymes were determined, and expression levels of genes involved in citric acid metabolism were measured by quantitative real-time PCR (qRT-PCR). The results revealed that citric acid was the predominant organic acid in both cultivars during fruit development. The highest citric acid contents were observed in both cultivars at green stage, which were reduced with fruit maturity. Correlation analysis showed that citrate synthase (CS), cytosolic aconitase (Cyt-ACO) and cytosolic isocitrate dehydrogenase (Cyt-IDH) may be involved in regulating citric acid biosynthesis. Meanwhile, the PeCS2, PeACO4, PeACO5 and PeIDH1 genes may play an important role in regulating the accumulation of citric acid. This study provides new insights for future elucidation of key mechanisms regulating organic acid biosynthesis in passion fruit.

Chemical Sensors for Farm-to-Table Monitoring of Fruit Quality

Farm-to-table operations produce, transport, and deliver produce to consumers in very different ways than conventional, corporate-scale agriculture operations. As a result, the time it takes to get a freshly picked fruit to the consumer is relatively short and the expectations of the consumer for freshness and quality are high. Since many of these operations involve small farms and small businesses, resources to deploy sensors and instruments for monitoring quality are scarce compared to larger operations. Within stringent power, cost, and size constraints, this article analyzes chemical sensor technologies suitable for monitoring fruit quality from the point of harvest to consumption in farm-to-table operations. Approaches to measuring sweetness (sugar content), acidity (pH), and ethylene gas are emphasized. Not surprisingly, many instruments developed for laboratory use or larger-scale operations are not suitable for farm-to-table operations. However, there are many opportunities still available to adapt pH, sugar, and ethylene sensing to the unique needs of localized farm-to-table operations that can help these operations survive and expand well into the future.

The Use of a Micro Near Infrared Portable Instrument to Predict Bioactive Compounds in a Wild Harvested Fruit-Kakadu Plum (Terminalia ferdinandiana)

Kakadu plum (KP; Terminalia ferdinandiana Exell, Combretaceae) is an emergent indigenous fruit originating from Northern Australia, with valuable health and nutritional characteristics and properties (e.g., high levels of vitamin C and ellagic acid). In recent years, the utilization of handheld NIR instruments has allowed for the in situ quantification of a wide range of bioactive compounds in fruit and vegetables. The objective of this study was to evaluate the ability of a handheld NIR spectrophotometer to measure vitamin C and ellagic acid in wild harvested KP fruit samples. Whole and pureed fruit samples were collected from two locations in the Kimberley region (Western Australia, Australia) and were analysed using both reference and NIR methods. The standard error in cross validation (SECV) and the residual predictive deviation (RPD) values were 1.81% dry matter (DM) with an RPD of 2.1, and 3.8 mg g⁻¹ DM with an RPD of 1.9 for the prediction of vitamin C and ellagic acid, respectively, in whole KP fruit. The SECV and RPD values were 1.73% DM with an RPD of 2.2, and 5.6 mg g⁻¹ DM with an RPD of 1.3 for the prediction of vitamin C and ellagic acid, respectively, in powdered KP samples. The results of this study demonstrated the ability of a handheld NIR instrument to predict vitamin C and ellagic acid in whole and pureed KP fruit samples. Although the RPD values obtained were not considered adequate to quantify these bioactive compounds (e.g., analytical quantification), this technique can be used as a rapid tool to screen vitamin C in KP fruit samples for high and low quality vitamin C.

An efficient method to reduce grain angle influence on NIR spectra for predicting extractives content from heartwood stem cores of Toona. sinensis

BACKGROUND

A fast, reliable and non-destructive method is needed to qualify the extractives content (EC) in heartwood of T. sinensis cores in the breeding program for studying the genetic effect on EC. However, the influence of grain angle on near infrared (NIR) spectra prediction model for EC is unclear. In this study, NIR spectra were collected from both cross section and radial section of wood core samples in order to predict the EC in heartwood.

RESULTS

The effect of grain angle on calibration EC model was studied. Several different spectra pre-processing methods were implemented for calibration. It was found that standard normal variation (SNV) followed by 1st derivative yielded the best calibration result for T. sinensis EC. Grain angle had a significant influence on the predicted model for EC when using the whole NIR spectra. However, after testing a certain point of the prior variables for EC that were selected by the significant multivariate correlation (sMC), the influence of grain angle was significantly eliminated.

CONCLUSIONS

It is suggested that NIR spectroscopy is a promising method to predict EC in the solid wood without effecting grain angle.

Determination of pesticide residual levels in strawberry (Fragaria) by near-infrared spectroscopy

BACKGROUND

In this study, an infrared-based prediction method was developed for easy, fast and non-destructive detection of pesticide residue levels measured by reference analysis in strawberry (Fragaria × ananassa Duch, cv. Albion) samples using near-infrared spectroscopy and demonstrating its potential alternative or complementary use instead of traditional pesticide determination methods. Strawberries of Albion variety, which were supplied directly from greenhouses, were used as the study material. A total of 60 batch sample groups, each consisting of eight strawberries, was formed, and each group was treated with a commercial pesticide at different concentrations (26.7% boscalid + 6.7% pyraclostrobin) and varying residual levels were obtained in strawberry batches. The strawberry samples with pesticide residuals were used both to collect near-infrared spectra and to determine reference pesticide levels, applying QuEChERS (quick, easy, cheap, rugged, safe) extraction, followed by liquid chromatographic-mass spectrometric analysis.

RESULTS AND CONCLUSION

Partial least squares regression (PLSR) models were developed for boscalid and pyraclostrobin active substances. During model development, the samples were randomly divided into two groups as calibration (n = 48) and validation (n = 12) sets. A calibration model was developed for each active substance, and then the models were validated using cross-validation and external sets. Performance evaluation of the PLSR models was evaluated based on the residual predictive deviation (RPD) of each model. An RPD of 2.28 was obtained for boscalid, while it was 2.31 for pyraclostrobin. These results indicate that the developed models have reasonable predictive power. © 2019 Society of Chemical Industry.

Control of ascorbic acid in fortified powdered soft drinks using near-infrared spectroscopy (NIRS) and multivariate analysis

Powdered soft drinks (PSDs), fortified with antioxidants such as ascorbic acid (AA), are normally controlled by titration or chromatographic methods. This study evaluated the feasibility of using near-infrared spectroscopy (NIRS) and multivariate analysis to predict AA contents in PSDs as an alternative not-destructive method. The AA content of sixty-seven samples of commercial fortified grape and passion fruit PSDs was analyzed by the standard method (titration) and showed significant variance between flavors within the same brand. In addition, 75% of the samples required from 0.3 to 10.2 more cups of grape than passion fruit flavor to supply the AA Reference Nutrient Intake for children and adults. Spectral and reference data sets were split into calibration and validation sets. Partial least squares regression models were built and validated for the determination of AA in both PSDs. The model's basic statistics for grape flavor PSDs (RMSEC = 0.49 mg g^-1, R_cal ² = 0.84; RMSECV = 0.67 mg g^-1, R_CV ² = 0.70; RMSEP = 0.50 mg g^-1, R_pred ² = 0.84), and that for passion fruit flavor PSDs (RMSEC = 0.24 mg g^-1, R_cal ² = 0.95; RMSECV = 0.56 mg g^-1, R_CV ² = 0.76; RMSEP de 0.57 mg g^-1, R_pred ² = 0.72) indicated that NIRS-PLS methodology produced reasonable results. The limits of detection and quantification obtained showed that the method is useful to detect and quantify AA in the studied samples. A new set of grape drinks was used for external prediction and the RMSEP was 0.62 mg g^-1, R_pred ² was 0.72. Based on the results, NIRS-multivariate analysis proved to be useful for quality control of AA in commercialized grape and passion fruit in PSDs and a faster, objective and environmentally friendly method alternative to standard methods.

Effects of variable selection and processing of NIR and ATR-IR spectra on the prediction of extractive content in Eucalyptus bosistoana heartwood

The use of quick attenuated total reflectance infrared (ATR-IR) spectroscopy and near infrared (NIR) spectroscopy to predict extractives content (EC) in heartwood of E. bosistoana with partial least squares regression (PLSR) models was studied. Different spectra pre-processing methods and variable selection were tested for calibration optimisation. While variable selection substantially improved the NIR-PLSR models, only small effects were observed for spectra pre-processing methods and ATR-IR-PLSR models. Both of the NIR-PLSR and ATR-IR-PLSR models yielded reliably EC results with high R² and low root mean square error (RMSE). NIR based models performed better (RMSE 0.9%) than ATR-IR based models (RMSE 1.6%). Analysis showed that the models were based on IR signals assigned to chemical structures known from eucalyptus heartwood extracts. Combined with PLSR and variable selection, both, ATR-IR and the NIR spectroscopy, can be used to quickly predict EC in E. bosistoana, a measure needed in tree breeding and the quality control of for durable timber.

Applications of Non-destructive Technologies for Agricultural and Food Products Quality Inspection

The quality and safety of food is an increasing concern for worldwide business. Non-destructive methods (NDM), as a means of assessment and instrumentation have created an esteemed value in sciences, especially in food industries. Currently, NDM are useful because they allow the simultaneous measurement of chemical and physical data from food without destruction of the substance. Additionally, NDM can obtain both quantitative and qualitative data at the same time without separate analyses. Recently, many studies on non-destructive detection measurements of agro-food products and final quality assessment of foods were reported. As a general statement, the future of using NDM for assessing the quality of food and agricultural products is bright; and it is possible to come up with interesting findings through development of more efficient and precise imaging systems like the machine vision technique. The present review aims to discuss the application of different non-destructive methods (NDM) for food quality and safety evaluation.

Pattern recognition-based optical technique for non-destructive detection of Ectomyelois ceratoniae infestation in pomegranates during hidden activity of the larvae

In this research, the feasibility of utilizing visible/near-infrared (Vis/NIR) spectroscopy as an optical non-destructive technique combined with both supervised and unsupervised pattern recognition methods was assessed for detection of Ectomyelois ceratoniae, carob moth, infestation in pomegranates during hidden activity of the larvae. To this end, some fruits were artificially contaminated to the carob moth larvae. Vis/NIR spectra of the blank samples and the contaminated pomegranates without and with external visual symptoms of larvae infestation were analyzed one and two weeks after contaminating the samples as three groups of "Healthy", "Unhealthy-A" and "Unhealthy-B", respectively. Principal component analysis (PCA) as unsupervised pattern recognition method was used to verify the possibility of clustering of the pomegranate samples into the three groups. Discriminant analysis (DA) based on PCA was also used as a powerful supervised pattern recognition method to classify the samples. The calibration models of linear, quadratic and Mahalanobis discriminant analyses were developed based on different spectral pre-processing techniques. The best PCA-DA model was obtained using Mahalanobis distance method and first derivative (D1) pre-processing. The total percentage of correctly classified samples with the best calibration model was 97.9%. The developed model could also predict unknown samples with total percentage of correctly classified samples of 90.6%. It was concluded that Vis/NIR spectroscopy combined with pattern recognition method of PCA-DA can be an appropriate and rapid technology for non-destructively screening the pomegranates for detection of carob moth infestation during hidden activity of the larvae.

Quality evaluation of frozen guava and yellow passion fruit pulps by NIR spectroscopy and chemometrics

The present study investigated the application of near infrared spectroscopy as a green, quick, and efficient alternative to analytical methods currently used to evaluate the quality (moisture, total sugars, acidity, soluble solids, pH and ascorbic acid) of frozen guava and passion fruit pulps. Fifty samples were analyzed by near infrared spectroscopy (NIR) and reference methods. Partial least square regression (PLSR) was used to develop calibration models to relate the NIR spectra and the reference values. Reference methods indicated adulteration by water addition in 58% of guava pulp samples and 44% of yellow passion fruit pulp samples. The PLS models produced lower values of root mean squares error of calibration (RMSEC), root mean squares error of prediction (RMSEP), and coefficient of determination above 0.7. Moisture and total sugars presented the best calibration models (RMSEP of 0.240 and 0.269, respectively, for guava pulp; RMSEP of 0.401 and 0.413, respectively, for passion fruit pulp) which enables the application of these models to determine adulteration in guava and yellow passion fruit pulp by water or sugar addition. The models constructed for calibration of quality parameters of frozen fruit pulps in this study indicate that NIR spectroscopy coupled with the multivariate calibration technique could be applied to determine the quality of guava and yellow passion fruit pulp.

Evaluation of chemical components and properties of the jujube fruit using near infrared spectroscopy and chemometrics

Near-infrared spectroscopy (NIRS) calibrations were developed for the discrimination of spectra of the jujube (Zizyphus jujuba Mill.) fruit samples from four geographical regions. Prediction models were developed for the quantitative prediction of the contents of jujube fruit, i.e., total sugar, total acid, total phenolic content, and total antioxidant activity. Four pattern recognition methods, principal component analysis (PCA), linear discriminant analysis (LDA), least squares-support vector machines (LS-SVM), and back propagation-artificial neural networks (BP-ANN), were used for the geographical origin classification. Furthermore, three multivariate calibration models based on the standard normal variate (SNV) pretreated NIR spectroscopy, partial least squares (PLS), BP-ANN, and LS-SVM were constructed for quantitative analysis of the four analytes described above. PCA provided a useful qualitative plot of the four types of NIR spectra from the fruit. The LS-SVM model produced best quantitative prediction results. Thus, NIR spectroscopy in conjunction with chemometrics, is a very useful and rapid technique for the discrimination of jujube fruit.