Zaukuu John-Lewis Zinia, Mensah Sheila, Mensah Eric Tetteh, Akomanin-Mensah Florence, Wiredu Solomon, Kovacs Zoltan
Department of Food Science and Technology, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Department of Measurements and Process Control, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, Somlói út 14-16, Budapest, Hungary.
NPJ Sci Food. 2024 Oct 26;8(1):86. doi: 10.1038/s41538-024-00327-1.
Calcium carbide is prohibited as a fruit ripening agent in many countries due to its harmful effects. Current methods for detecting calcium carbide in fruit involve time-consuming and destructive chemical analysis techniques, necessitating the need for non-destructive and rapid detection techniques. This study combined near infrared (NIR) spectroscopy with chemometrics to detect two banana varieties ripened with calcium carbide in different forms when they are peeled or unpeeled. Sixteen linear discriminant analysis (LDA) models were developed with high average classification accuracies for classifying banana based on the mode used to ripen banana, type of carbide treatment and the duration of soaking banana in carbide solution. Banana colour was predicted with partial least squared regression (PLSR) models with RCV > 0.74, RMSECV and <5.4 and RPD close to 3. NIR coupled with chemometrics has good potential as a technique for detecting carbide ripened banana even if the banana is peeled or not.
由于其有害影响,许多国家禁止将电石用作水果催熟剂。目前检测水果中电石的方法涉及耗时且具有破坏性的化学分析技术,因此需要无损且快速的检测技术。本研究将近红外(NIR)光谱与化学计量学相结合,以检测两种不同品种的香蕉在去皮或未去皮时用不同形式的电石催熟后的情况。开发了16个线性判别分析(LDA)模型,这些模型在基于香蕉催熟方式、电石处理类型以及香蕉在电石溶液中的浸泡时间对香蕉进行分类时具有较高的平均分类准确率。使用偏最小二乘回归(PLSR)模型预测香蕉颜色,其交叉验证相关系数(RCV)>0.74,交叉验证均方根误差(RMSECV)<5.4,预测偏差比(RPD)接近3。即使香蕉去皮与否,近红外光谱与化学计量学相结合作为一种检测电石催熟香蕉的技术具有良好的潜力。