Unraveling the effects of drying techniques on chaya leaves: Metabolomics analysis of nonvolatile and volatile metabolites, umami taste, and antioxidant capacity.

Chaya (Cnidoscolus chayamansa) leaves are known for their strong umami taste and widespread use as a dried seasoning. This study aimed to assess the impact of different drying methods [freeze drying (FD), vacuum drying, oven drying at 50 °C and 120 °C (OD120) and pan roasting (PR)] on the metabolome using mass spectrometry, umami intensity, and antioxidant properties of chaya leaves. The predominant volatile compound among all samples, 3-methylbutanal, exhibited the highest relative odor activity value (rOAV), imparting a malt-like odor, while hexanal (green grass-like odor) and 2-methylbutanal (coffee-like odor) are the second highest rOAV in the FD and PR samples, respectively. OD120 and PR samples possessed the highest levels of umami-tasting amino acids and 5'-ribonucleotides as well as the most intense umami taste, whereas FD samples exhibited the highest antioxidant capacity. These findings enhance our understanding of the aroma characteristics, umami taste, and antioxidant potential of processed chaya leaves.

Hyperspectral imaging coupled with machine learning for classification of anthracnose infection on mango fruit.

Anthracnose is the major plant disease causing an economic loss of mango fruit. Anthracnose symptom is not visible at a quiescent stage and the infected fruit often enters the food chain before the infection is known. Detection of a pre-symptomatic anthracnose infection is thus, crucial to prevent the infected fruit from entering the food chain. This research applied hyperspectral imaging (HSI) spectroscopy integrated with machine learning (ML) including principal component analysis (PCA) and support vector machine (SVM) for rapid identification of quiescent infection of anthracnose in mango fruit. Mango fruit (Nam Dok Mai Si Thong) was artificially infected with Colletotrichum gloeosporioides and stored at 20 °C and 90 % RH. The HSI was used to collect the spectral and spatial data of the samples. PCA and SVM were respectively performed to explore the hyperspectral data and to classify different symptom severities. The obtained spectral data can be recognized as fingerprints ascribing to the metabolites produced by C. gloeosporioides and the decomposed fruit tissues caused by the fungal infection. The HSI integrated with ML was able to not only detect the anthracnose infection at a latent stage before the onset of disease symptoms but also correctly classify different symptom severities. The symptom maps were also constructed using false-color image processing to simplify the data visualization of different symptom severities. The capability of detecting a pre-symptomatic anthracnose infection is a key advantage of the developed ML-assisted HSI.

Green active coating from chitosan incorporated with spontaneous cinnamon oil nanoemulsion: Effects on dried shrimp quality and shelf life.

Green active film from chitosan (C) incorporated with spontaneous emulsified cinnamon oil nanoemulsion (CONE; droplet size of 79.27 nm and polydispersity index of 0.27) was developed. The obtained chitosan film containing CONE (C + CONE) had tensile elongation and light protective effect higher than C film due to the incorporation of bioactive compounds from cinnamon oil as proven by Fourier Transform Infrared Spectroscopy. The effect of C + CONE as active edible coating on the physical, chemical, and microbiological properties of dried shrimp was then investigated. The quality of samples coated with C + CONE (DS + C + CONE) was compared to those coated with C (DS + C) and without coating (DS). In this study, C + CONE could enhance astaxanthin content and reduce lipid oxidation in dried shrimp. During 6 weeks of storage, C + CONE was found to be an effective antimicrobial coating that significantly inhibited growth of bacteria, delayed lipid oxidation and retarded the production of volatile amines in dried shrimp. DS + C + CONE had lower malonaldehyde equivalents (0.52 mg/kg oil), trimethylamine (11.74 mg/100 g), total volatile base nitrogen (84.33 mg/100 g) and total viable count (4.80 Log CFU/g), but had higher astaxanthin content (12.53 ± 0.12 µg/g) than DS and DS + C. The results suggested that the developed C + CONE coating has potential to be used as active coating for preserving food quality.

Biodegradable active coating from chitosan/astaxanthin crosslinked with genipin to improve water resistance, moisture and oxygen barrier and mechanical properties of Kraft paper.

An active chitosan-based coating with an addition of a natural antioxidant and a natural crosslinker has been applied to improve the performance of Kraft paper. Coatings, including chitosan (CS), CS crosslinked with 1.5 % genipin (CS-1.5G), CS containing 1 % astaxanthin (CSA) and CSA crosslinked with 1 % genipin (CSA-1G) were coated on Kraft paper. Uncoated and coated papers were then evaluated for water content (WC), water vapor permeability (WVP), contact angle, oxygen permeability (OP), tensile strength (TS), bursting strength and elongation at break (EAB). Results indicated that the coating improved the properties of Kraft paper. When compared with CS-coated paper, WC and WVP of CSA-1G-coated paper decreased significantly by 40 % and 24 %, respectively. The lowest OP was also observed in CSA-1G-coated paper. CSA- and CSA-1G-coated paper had contact angle values >100°, indicating hydrophobic nature of coated paper surface. All coatings largely improved TS of the paper (by 182-224 %) whereas CS-1.5G and CSA-1G significantly improved the bursting strength of the paper. The developed genipin-crosslinked chitosan-based coating enriched with astaxanthin can improve the functional properties of Kraft paper and thus will facilitate the usage of the paper for food packaging applications.

Metabolite profiling and identification of novel umami compounds in the chaya leaves of two species using multiplatform metabolomics.

Chaya (Cnidoscolus chayamansa and C. aconitifolius) is a fast-growing medicinal plant, and its leaves exhibit a strong umami taste. Here metabolite variation and umami-related compounds in the leaves of two chaya species were determined using a multiplatform untargeted-metabolomics approach, electronic tongue, and in silico screening. Metabolite profiles varied between the leaves of the two species and among leaf maturation stages. Young leaves exhibited the highest umami taste intensity, followed by mature and old leaves. Partial least square regression and computational molecular docking analyses revealed five potent umami substances (quinic acid, trigonelline, alanyl-tyrosine, leucyl-glycyl-proline, and leucyl-aspartyl-glutamine) and three known umami compounds (l-glutamic acid, pyroglutamic acid, and 5'-adenosine monophosphate). The five substances were validated as novel umami compounds using electronic tongue assay; leucyl-glycyl-proline exhibited synergism with monosodium glutamate, thereby enhancing the umami taste. Thus, substances contributing to the taste of chaya leaves were successfully identified.

Response surface methodology for optimization of cinnamon essential oil nanoemulsion with improved stability and antifungal activity.

The optimum formulation and ultrasonic condition for fabrication of cinnamon essential oil (CEO) nanoemulsion were determined using Response Surface Methodology (RSM). The CEO nanoemulsions were formed using an ultrasonic bath (43 kHz at power output of 210 W) and an ultrasonic probe (24 kHz at power of 400 W). Probe ultrasonication outperformed bath ultrasonication since it produced nanoemulsions with smaller droplet size, narrower size distribution as measured using polydispersity index (PDI), and higher viscosity. The influences of sonication time of 180.23-351.77 s, temperature of 4.82-45.18 °C, and Tween® 80 concentration of 1-3% w/w on the droplet size, PDI, and viscosity were investigated using RSM based on Box-Behnken design (BBD). The RSM revealed that the sonication time of 266 s, temperature of 4.82 °C, and Tween® 80 of 3% w/w produced the optimum CEO nanoemulsion with droplet size of 65.98 nm, PDI of 0.15, and viscosity of 1.67 mPa.s. Moreover, the optimum nanoemulsion had good stability in terms of droplet size and PDI when storing at 4, 30, and 45 °C for 90 days. The antifungal activity of the optimum CEO nanoemulsion was then investigated against Aspergillus niger, Rhizopus arrhizus, Penicillium sp., and Colletotrichum gloeosporioides in comparison to CEO coarse emulsion. The results showed that the CEO nanoemulsion had better antifungal activity than coarse emulsion of CEO.

Simultaneous assessment of various quality attributes and shelf life of packaged bratwurst using hyperspectral imaging.

A simultaneous evaluation of various quality attributes of packaged bratwurst using hyperspectral imaging (HSI) was developed. Changes in physicochemical (L*, a*, b* color values, pH and thiobarbituric acid (TBA)), microbiological (total viable counts (TVC) and lactic acid bacteria (LAB)) and sensory (color, odor and overall acceptability) characteristics of the packaged sausages were monitored during storage at 4 ±â€¯1 °C. Reflectance spectra covering a wavelength range of 400-1000 nm of the samples were acquired using HSI. The relationships between the quality attributes and the spectroscopic reflectance were investigated using canonical correlation analysis. Among all quality attributes, L* color value, TBA, TVC, LAB, odor and overall acceptability appeared to be highly associated with the reflectance. To facilitate the HSI for rapid image acquisition and data processing, partial least squares regression (PLSR) analysis was employed for selection of optimal wavelengths. The selected wavelengths were then assembled into multispectral data and used as input variables to optimize the PLSR and artificial neural network models for the prediction of quality attributes of the sausage samples. The HSI technique can be used for rapid and nondestructive evaluation of the product's quality and shelf life.

Active packaging from chitosan-titanium dioxide nanocomposite film for prolonging storage life of tomato fruit.

The feasibility of active packaging from chitosan (CS) and chitosan containing nanosized titanium dioxide (CT) to maintain quality and extend storage life of climacteric fruit was investigated. The CT nanocomposite film and CS film were fabricated using a solution casting method and used as active packaging to delay ripening process of cherry tomatoes. Changes in firmness, weight loss, a*/b* color, lycopene content, total soluble solid, ascorbic acid, and concentration of ethylene and carbon dioxide of the tomatoes packaged in CT film, CS film, and control (without CT or CS films) were monitored during storage at 20°C. Classification of fruit quality as a function of different packaging treatments was visualized using linear discriminant analysis. Tomatoes packaged in the CT film evolved lower quality changes than those in the CS film and control. The results suggested that the CT film exhibited ethylene photodegradation activity when exposed to UV light and consequently delayed the ripening process and changes in the quality of the tomatoes.

Optimization of cinnamon oil nanoemulsions using phase inversion temperature method: Impact of oil phase composition and surfactant concentration.

Essential oils, such as those isolated from cinnamon, are effective natural antimicrobial agents, but their utilization is limited by their low water-solubility. In this study, phase inversion temperature (PIT) was used to prepare cinnamon oil nanoemulsions. To this aim, it was hypothesized that cinnamon oil nanoemulsions could be fabricated by optimizing the oil phase composition and surfactant concentration of the system and their stability could be enhanced using a cooling-dilution method during the PIT. A mixture of cinnamon oil, non-ionic surfactant, and water was heated above the PIT of the system, and then rapidly cooled with continuous stirring, which led to the spontaneous generation of small oil droplets. The impact of oil phase composition and surfactant concentration on the formation and stability of the nanoemulsions was determined. Cinnamon oil nanoemulsions with the smallest mean droplet diameter (101 nm) were formed using 40:60 wt% of cinnamon oil and medium chain triglyceride (MCT) in the total lipid phase. Increasing surfactant concentration significantly decreased the mean droplet diameter of the nanoemulsions but did not alter their particle morphology. In addition, using the cooling-dilution method, the nanoemulsions were stable for at least 31 days when stored at 4 °C or 25 °C.

Data visualization of Salmonella Typhimurium contamination in packaged fresh alfalfa sprouts using a Kohonen network.

Class visualization of multi-dimensional data from analysis of volatile metabolic compounds monitored using an electronic nose based on metal oxide sensor array was attained using a Kohonen network. An array of 12 metal oxide based chemical sensors was used to monitor changes in the volatile compositions from the headspace of packaged fresh sprouts with and without Salmonella Typhimurium contamination. Kohonen׳s self-organizing map (SOM) was then created for learning different patterns of volatile metabolites. The Kohonen network comprising 225 nodes arranged into a two-dimensional hexagonal map was used to locate the samples on the map to facilitate sample classification. Graphical maps including the unified matrix, component planes, and hit histograms were described to characterize the relation between samples. The clustering of samples with different levels of S. Typhimurium contamination could be visually distinguishable on the SOM. The Kohonen network proved to be advantageous in visualization of multi-dimensional nonlinear data and provided a clearer separation of different sample groups than a conventional linear principal component analysis (PCA) approach. The sensor array integrated with the Kohonen network could be used as a rapid and nondestructive method to distinguish samples with different levels of S. Typhimurium contamination. Although the analyses were performed on samples with natural background microbiota of about 7 Log(CFU/g), this microbiota did not affect the S. Typhimurium detection. The proposed method has potential to rapidly detect a target foodborne pathogen in real-life food samples instantaneously without subsequently culturing stages.

Solid phase microextraction/gas chromatography/mass spectrometry integrated with chemometrics for detection of Salmonella typhimurium contamination in a packaged fresh vegetable.

A rapid method for detection of Salmonella typhimurium contamination in packaged alfalfa sprouts using solid phase microextraction/gas chromatography/mass spectrometry (SPME/GC/MS) integrated with chemometrics was investigated. Alfalfa sprouts were inoculated with S. typhimurium, packed into commercial LDPE bags and stored at 10+2 degrees C for 0, 1, 2 and 3 days. Uninoculated sprouts were used as control samples. A SPME device was used to collect the volatiles from the headspace above the samples and the volatiles were identified using GC/MS. Chemometric techniques including linear discriminant analysis (LDA) and artificial neural network (ANN) were used as data processing tools. Numbers of Salmonella were followed using a colony counting method. From LDA, it was able to differentiate control samples from sprouts contaminated with S. typhimurium. The potential to predict the number of contaminated S. typhimurium from the SPME/GC/MS data was investigated using multilayer perceptron (MLP) neural network with back propagation training. The MLP comprised an input layer, one hidden layer, and an output layer, with a hyperbolic tangent sigmoidal transfer function in the hidden layer and a linear transfer function in the output layer. The MLP neural network with a back propagation algorithm could predict number of S. typhimurium in unknown samples using the volatile fingerprints. Good prediction was found as measured by a regression coefficient (R(2)=0.99) between actual and predicted data.

Detection of Escherichia coli in packaged alfalfa sprouts with an electronic nose and an artificial neural network.

A rapid method for the detection of Escherichia coli (ATCC 25922) in packaged alfalfa sprouts was developed. Volatile compounds from the headspace of packaged alfalfa sprouts, inoculated with E. coli and incubated at 10 degrees C for 1, 2, and 3 days, were collected and analyzed. Uninoculated sprouts were used as control samples. An electronic nose with 12 metal oxide electronic sensors was used to monitor changes in the composition of the gas phase of the package headspace with respect to volatile metabolites produced by E. coli. The electronic nose was able to differentiate between samples with and without E. coli. To predict the number of E. coli in packaged alfalfa sprouts, an artificial neural network was used, which included an input layer, a hidden layer, and an output layer, with a hyperbolic tangent sigmoidal transfer function in the hidden layer and a linear transfer function in the output layer. The network was shown to be capable of correlating voltametric responses with the number of E. coli. A good prediction was possible, as measured by a regression coefficient (R2 = 0.903) between the actual and predicted data. In conjunction with the artificial neural network, the electronic nose proved to have the ability to detect E. coli in packaged alfalfa sprouts.