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通过气相色谱-质谱联用分析储存马铃薯(L.)中的细菌和真菌感染来发现挥发性特征

Spotting of Volatile Signatures through GC-MS Analysis of Bacterial and Fungal Infections in Stored Potatoes ( L.).

作者信息

Kate Adinath, Tiwari Shikha, Gujar Jamna Prasad, Modhera Bharat, Tripathi Manoj Kumar, Ray Hena, Ghosh Alokesh, Mohapatra Debabandya

机构信息

ICAR-Central Institute of Agricultural Engineering, Nabibagh, Berasia Road, Bhopal 462038, India.

Maulana Azad National Institute of Technology, Bhopal 462003, India.

出版信息

Foods. 2023 May 22;12(10):2083. doi: 10.3390/foods12102083.

DOI:10.3390/foods12102083
PMID:37238902
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10216920/
Abstract

Potatoes inoculated with spp., and , along with healthy (control) samples, were stored at different storage temperatures (4 ± 1 °C, 8 ± 1 °C, 25 ± 1 °C) for three weeks. Volatile organic compounds (VOCs) were mapped using the headspace gas analysis through solid phase micro extraction-gas chromatography-mass spectroscopy every week. The VOC data were arranged into different groups and classified using principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) models. Based on a variable importance in projection (VIP) score > 2 and the heat map, prominent VOCs were identified as 1-butanol and 1-hexanol, which can act as biomarkers for related bacterial spoilage during storage of potatoes in different conditions. Meanwhile, hexadecanoic acid and acetic acid were signature VOCs for , and hexadecane, undecane, tetracosane, octadecanoic acid, tridecene and undecene were associated with . The PLS-DA model performed better at classifying the VOCs of the three different species of infection and the control sample compared to PCA, with high values of R (96-99%) and Q (0.18-0.65). The model was also found to be reliable for predictability during random permutation test-based validation. This approach can be adopted for fast and accurate diagnosis of pathogenic invasion of potatoes during storage.

摘要

用……接种的马铃薯,以及……,与健康(对照)样本一起,在不同储存温度(4±1℃、8±1℃、25±1℃)下储存三周。每周通过固相微萃取-气相色谱-质谱联用的顶空气体分析对挥发性有机化合物(VOCs)进行图谱绘制。将VOC数据整理成不同组,并使用主成分分析(PCA)和偏最小二乘判别分析(PLS-DA)模型进行分类。基于投影变量重要性(VIP)得分>2和热图,确定突出的VOCs为1-丁醇和1-己醇,它们可作为不同条件下马铃薯储存期间与……相关细菌腐败的生物标志物。同时,十六烷酸和乙酸是……的标志性VOCs,十六烷、十一烷、二十四烷、十八烷酸、十三烯和十一烯与……有关。与PCA相比,PLS-DA模型在对三种不同感染物种的VOCs和对照样本进行分类时表现更好,R值较高(96-99%),Q值较高(0.18-0.65)。在基于随机排列测试的验证过程中,该模型在可预测性方面也被发现是可靠的。这种方法可用于快速准确诊断马铃薯储存期间的病原菌入侵。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06cd/10216920/c3adf064b4db/foods-12-02083-g004a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06cd/10216920/371b64a51c15/foods-12-02083-g001a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06cd/10216920/2782b950fd74/foods-12-02083-g002a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06cd/10216920/4007b92c354a/foods-12-02083-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06cd/10216920/c3adf064b4db/foods-12-02083-g004a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06cd/10216920/371b64a51c15/foods-12-02083-g001a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06cd/10216920/2782b950fd74/foods-12-02083-g002a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06cd/10216920/4007b92c354a/foods-12-02083-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06cd/10216920/c3adf064b4db/foods-12-02083-g004a.jpg

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