Chemical structure, composition, bioactive compounds, and pattern recognition techniques in figs (Ficus carica L.) quality and authenticity: An updated review

IF 4 2区 农林科学 Q2 CHEMISTRY, APPLIED
Elahesadat Hosseini , Zenebe Tadesse Tsegay , Slim Smaoui , Theodoros Varzakas
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Abstract

Figs (Ficus carica L.) are a prominent agricultural product in the Mediterranean and Middle Eastern regions, valued for their rich phytochemical composition and potential health benefits. This review systematically explores the chemical composition of Ficus carica L., focusing on bioactive compounds such as phenolic acids, flavonoids, and carotenoids. It also emphasizes the role of advanced analytical techniques, such as Near-Infrared (NIR) and Fourier-Transform Infrared (FTIR) spectroscopy, coupled with chemometrics and bioinformatics, in assessing fig quality and authenticity. These techniques offer significant advantages as rapid, non-destructive methods for distinguishing between different varieties of Ficus carica L., and detecting potential adulteration. Chemometric analyses, such as principal component analysis (PCA) and partial least squares regression (PLSR), have proven essential in processing complex datasets, enhancing quality control and variety differentiation accuracy. For example, PLSR models achieved an R² of 0.92–0.99 for assessing phenolic compounds in figs' peel and pulp extracts. Moreover, this review emphasizes that dark-skinned varieties of Ficus carica L., exhibit higher concentrations of phenolic compounds, with values reaching up to 444 mg of phenols per 40 g of fresh fruit. Integrating these advanced analytical techniques and molecular tools offers a robust framework for developing validated approaches to fig authentication, ensuring food safety, and enhancing market trust through protected geographical indications.
无花果(Ficus carica L.)质量和真实性的化学结构、成分、生物活性化合物和模式识别技术:最新综述
无花果(Ficus carica L.)是地中海和中东地区的主要农产品,因其丰富的植物化学成分和潜在的健康益处而备受重视。本综述系统地探讨了无花果的化学成分,重点是酚酸、类黄酮和类胡萝卜素等生物活性化合物。文章还强调了近红外(NIR)和傅立叶变换红外(FTIR)光谱等先进分析技术,以及化学计量学和生物信息学在评估无花果质量和真实性方面的作用。这些技术作为快速、无损的方法,在区分无花果不同品种和检测潜在掺假方面具有显著优势。化学计量分析,如主成分分析(PCA)和偏最小二乘回归(PLSR),已被证明在处理复杂数据集、提高质量控制和品种区分准确性方面至关重要。例如,在评估无花果果皮和果肉提取物中的酚类化合物时,偏最小二乘法回归模型的 R² 值为 0.92-0.99。此外,这篇综述还强调,无花果的深色果皮品种表现出更高的酚类化合物浓度,每 40 克鲜果中的酚含量高达 444 毫克。整合这些先进的分析技术和分子工具为开发无花果鉴定的有效方法、确保食品安全和通过受保护的地理标志提高市场信任度提供了一个强大的框架。
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来源期刊
Journal of Food Composition and Analysis
Journal of Food Composition and Analysis 工程技术-食品科技
CiteScore
6.20
自引率
11.60%
发文量
601
审稿时长
53 days
期刊介绍: The Journal of Food Composition and Analysis publishes manuscripts on scientific aspects of data on the chemical composition of human foods, with particular emphasis on actual data on composition of foods; analytical methods; studies on the manipulation, storage, distribution and use of food composition data; and studies on the statistics, use and distribution of such data and data systems. The Journal''s basis is nutrient composition, with increasing emphasis on bioactive non-nutrient and anti-nutrient components. Papers must provide sufficient description of the food samples, analytical methods, quality control procedures and statistical treatments of the data to permit the end users of the food composition data to evaluate the appropriateness of such data in their projects. The Journal does not publish papers on: microbiological compounds; sensory quality; aromatics/volatiles in food and wine; essential oils; organoleptic characteristics of food; physical properties; or clinical papers and pharmacology-related papers.
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