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.

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.

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.