Physico-chemical and chemometric analysis of milk chocolate sold in Ghana using NIR spectroscopy

IF 7.2 Q1 FOOD SCIENCE & TECHNOLOGY
Vincent Abe-Inge , John-Lewis Zinia Zaukuu , Latifatu Mohammed , Jacob K. Agbenorhevi , Ibok Oduro
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引用次数: 0

Abstract

Chocolates sold in Ghana are stored under different conditions that are suspected to affect their appearance, flavour and texture. Rapid and non-invasive techniques such as near-infrared spectroscopy (NIRS) have been lauded for their reliability and cost-effectiveness that can be very useful for chocolate monitoring. This study developed a rapid and non-destructive method to predict the quality of chocolate obtained from three different sales outlets based on the location and conditions of retail. Data for physicochemical analysis (total color change, total phenolics, free fatty acid, peroxide value, moisture, hardness, and aluminum content) and mold count of chocolate were collected using standard protocols. These data and results obtained from NIRS in the wavelength range 900–1700 nm, were used to develop chemometric models to predict the parameters measured and classified the chocolate samples. Chocolate from the street recorded the highest mold count of 10.00 ± 18.92 cfu/g. Although the physicochemical analysis showed that different retail conditions had no significant effect on the chocolate quality parameters, the NIRS models could classify the chocolates based on retail conditions, with an average recognition and prediction accuracy of 75.41 % and 71.59 %, respectively. The regression model could predict the total color change with R2CV of 0.503 and RMSECV of 4.96 w/w. The findings suggest that NIRS combined with chemometrics could be used to classify chocolate sold under different conditions at different retail locations. However, the models could not predict other physicochemical quality parameters.

Abstract Image

利用近红外光谱对加纳销售的牛奶巧克力进行理化和化学计量分析
在加纳销售的巧克力在不同条件下储存,可能会影响其外观、风味和口感。近红外光谱(NIRS)等快速、非侵入性技术因其可靠性和成本效益而备受赞誉,对巧克力监测非常有用。本研究开发了一种快速、无损的方法,可根据零售地点和条件预测从三个不同销售点获得的巧克力的质量。采用标准方案收集了巧克力的理化分析数据(总变色、总酚类、游离脂肪酸、过氧化值、水分、硬度和铝含量)和霉菌计数。利用这些数据和波长范围为 900-1700 纳米的近红外光谱所获得的结果来建立化学计量模型,以预测所测量的参数并对巧克力样品进行分类。街头巧克力的霉菌计数最高,为 10.00 ± 18.92 cfu/g。虽然理化分析表明不同的零售条件对巧克力质量参数没有显著影响,但近红外光谱模型可以根据零售条件对巧克力进行分类,平均识别率和预测准确率分别为 75.41 % 和 71.59 %。回归模型可以预测总颜色变化,R2CV 为 0.503,RMSECV 为 4.96 w/w。研究结果表明,近红外光谱与化学计量学相结合可用于对不同零售地点不同条件下出售的巧克力进行分类。但是,这些模型无法预测其他理化质量参数。
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来源期刊
Future Foods
Future Foods Agricultural and Biological Sciences-Food Science
CiteScore
8.60
自引率
0.00%
发文量
97
审稿时长
15 weeks
期刊介绍: Future Foods is a specialized journal that is dedicated to tackling the challenges posed by climate change and the need for sustainability in the realm of food production. The journal recognizes the imperative to transform current food manufacturing and consumption practices to meet the dietary needs of a burgeoning global population while simultaneously curbing environmental degradation. The mission of Future Foods is to disseminate research that aligns with the goal of fostering the development of innovative technologies and alternative food sources to establish more sustainable food systems. The journal is committed to publishing high-quality, peer-reviewed articles that contribute to the advancement of sustainable food practices. Abstracting and indexing: Scopus Directory of Open Access Journals (DOAJ) Emerging Sources Citation Index (ESCI) SCImago Journal Rank (SJR) SNIP
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