Targeted versus Nontargeted Green Strategies Based on Headspace-Gas Chromatography-Ion Mobility Spectrometry Combined with Chemometrics for Rapid Detection of Fungal Contamination on Wheat Kernels

Early identification and monitoring of soft rot contamination in postharvest kiwifruit based on volatile fingerprints using headspace-gas chromatography-ion mobility spectrometry

Soft rot is one of the most devastating fungal diseases occurring in kiwifruit during postharvest storage. Whereas, the approaches for rapid, precise and non-destructive testing of early soft rot contamination are still scarce. This study successfully employed headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) to evaluate volatile organic compounds (VOCs) of soft rot kiwifruit contaminated with three pathogenic fungi including B. dothidea, Diaporthe spp., A. alternata. A total of 88, 106, 110 and 99 VOCs were detected in non-infected, B. dothidea, Diaporthe spp., A. alternata-infected kiwifruit, respectively. Specific fingerprints were established with 15, 26 and 11 characteristic VOCs emitted by three pathogenic fungi invading kiwifruit and not present in non-infected kiwifruit, accomplishing effective discrimination between healthy and disease kiwifruit. Additionally, alternative potential volatile biomarkers for soft rot kiwifruit were screened, such as α-terpinene, α-terpinolene, 3-p-menthanol, 3-hepten-2-one, ethyl 2-methylbutanoate, etc. These findings promised non-destructive and timely monitoring for disease occurrence during kiwifruit postharvest.

Real-time mildew detection and gradation in simulated containerized soybeans: Insights from GC-IMS analysis of mVOCs and VOCs

In the context of bulk grain container transportation, the complex logistics can lead to grain mildew and subsequent economic losses. Therefore, there is a pressing need to explore swift and real-time mildew detection technology. Our investigation, simulating actual transportation conditions, revealed that Aspergillus, Penicillium, and Rhizopus were the primary molds responsible for soybean mildew during container transportation. Utilizing gas chromatography-ion migration spectroscopy (GC-IMS), we analyzed the correlation between the mVOCs (microbial volatile organic compounds) produced by dominant mold and the VOCs emitted during soybean mildew. Principal Component Analysis (PCA) and clustering results demonstrated the distinctive identification of VOCs in soybeans with varying degrees of mildew. The mildew degree significantly influenced the content variation of VOCs. As the mildew degree increased, the concentrations of nonanal, octanal, etc. progressively decreased, contrasting with the rising levels of phenylacetaldehyde, 3-methyl-2-butenal, etc. Therefore, the combination of GC-IMS with chemometrics proves to be a viable method for identifying the mildew degree of soybeans. Therefore, this study underscores the importance of implementing effective mildew detection techniques in the challenging context of bulk grain container transportation.

Colorimetric sensor array combined with chemometric methods for the assessment of aroma produced during the drying of tencha

The aroma produced during drying is an important indicator of tencha and needs to be monitored. This study constructed an olfactory visualization system for assessing tencha aroma using colorimetric sensor array (CSA) combined with chemometric methods. The 16 chemically responsive dyes were selected to obtain aroma information of tencha samples and extracted image data of aroma information by machine vision algorithms. Subsequently, k-nearest neighbor, support vector machine, classification and regression tree, and random forest (RF), four qualitative models were applied to build the mathematical models. The RF model with nine principal components was preferred, with recognition rate of 100.00% and 91.07% for the training and prediction sets, respectively. The experimental results showed that CSA combined with the RF model can be effectively applied to assess tencha aroma. This study provided a scientific and novel method to maintain the stability of tencha quality in the production process.

Functional, Physical, and Volatile Characterization of Chitosan/Starch Food Films Functionalized with Mango Leaf Extract

Active packaging is one of the currently thriving methods to preserve highly perishable foods. Nonetheless, the integration of active substances into the formulation of the packaging may alter their properties-particularly mass transfer properties-and therefore, the active compounds acting. Different formulations of chitosan (CH), starch (ST), and their blends (CH-ST), with the addition of mango leaf extract (MLE) have been polymerized by casting to evaluate their food preservation efficiency. A CH-ST blend with 3% MLE using 7.5 mL of the filmogenic solution proved to be the most effective formulation because of its high bioactivity (ca. 80% and 74% of inhibition growth of S. aureus and E. coli, respectively, and 40% antioxidant capacity). The formulation reduced the water solubility and water vapor permeability while increasing UV protection, properties that provide a better preservation of raspberry fruit after 13 days than the control. Moreover, a novel method of Headspace-Gas Chromatography-Ion Mobility Spectrometry to analyze the volatile profiles of the films is employed, to study the potential modification of the food in contact with the active film. These migrated compounds were shown to be closely related to both the mango extract additions and the film's formulation themselves, showing different fingerprints depending on the film.

Volatile Metabolite Profiling of Wheat Kernels Contaminated by Fusarium graminearum

Traditional methods used to detect fungi or mycotoxins are time-consuming and prevent real-time monitoring. In this study, solid-phase microextraction combined with full two-dimensional gas chromatography time-of-flight mass spectrometry was utilized to detect volatile organic compounds (VOCs) produced by fungi during grain infestation predictive F. graminearum PH-1 infestation in wheat. The results show that the VOCs emitted by F. graminearum can distinguish strains at different growth stages. The growth matrices (potato dextrose agar medium and wheat kernels) play a large role in VOC production. The infection of wheat sample F. graminearum showed that a specific relationship between VOCs and the composition of fungal flora, for example, 5-pentyl-cyclohexa-1,3-diene, 3-hexanone, and 1,3-octadiene, was positively correlated with the infection rate of PH-1. In the correlation study of fungal mycotoxins and VOCs, zearalenone produced by F. graminearum was predicted based on the VOCs released. Further analysis determined the correlation of three VOCs, 6-butyl-1,4-cycloheptadiene, hexahydro-3-methylenebenzofuran-2(3H)-one, and (E,E)-3,5-octadien-2-one, with zearalenone production, confirming the ability of VOCs as characteristic markers of mycotoxins.

Non-Targeted Screening Approaches for Profiling of Volatile Organic Compounds Based on Gas Chromatography-Ion Mobility Spectroscopy (GC-IMS) and Machine Learning

Due to its high sensitivity and resolving power, gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful technique for the separation and sensitive detection of volatile organic compounds. It is a robust and easy-to-handle technique, which has recently gained attention for non-targeted screening (NTS) approaches. In this article, the general working principles of GC-IMS are presented. Next, the workflow for NTS using GC-IMS is described, including data acquisition, data processing and model building, model interpretation and complementary data analysis. A detailed overview of recent studies for NTS using GC-IMS is included, including several examples which have demonstrated GC-IMS to be an effective technique for various classification and quantification tasks. Lastly, a comparison of targeted and non-targeted strategies using GC-IMS are provided, highlighting the potential of GC-IMS in combination with NTS.

Volatile Profile and Biosynthesis of Post-harvest Apples are Affected by the Mechanical Damage

Mechanical damage to fruit causes flavor changes during post-harvest supply chains. It is important to identify the main volatiles and explore their biosynthesis mechanism. In this study, the volatile changes in apples caused by mechanical damage were analyzed by gas chromatography-ion mobility spectrometry. Hexanal and ethyl acetate were accumulated and identified as potential volatile biomarkers to detect damaged apples. The study on the lipoxygenase (LOX) pathway and transcription factors (TFs) shows that mechanical damage up-regulated the expression of MdLOX-like, MdLOX3b, MdLOX7b, MdLOX7c, MdLOX2a, and MdAAT in the LOX pathway and that of one MYB TF (MdMYB-like), five ERF TFs (MdERF073, MdERF003, MdERF114, MdERF15, and MdERF2), and five WRKY TFs (MdWRKY23, MdWRKY17, MdWRKY46, MdWRKY48, and MdWRKY71). Notably, MdAAT was significantly correlated to MdMYB-like, MdWRKY23, MdWRKY71, MdERF15, and MdERF2. Thus, TFs may attribute to the accumulation of hexanal and ethyl acetate by regulating the expression of LOX pathway-related genes.