A novel and simple method based on the chemometric treatment of UV–visible spectra of acetonitrile extracts to detect plant-derived adulterants in saffron (Crocus sativus L.)

IF 4 2区 农林科学 Q2 CHEMISTRY, APPLIED
Martina Foschi , Francesca Di Donato , Alessandra Biancolillo , Francesco D’Emilia , Maria Anna Maggi , Angelo Antonio D’Archivio
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Abstract

We developed a UV–visible spectroscopic method, inspired by ISO-3632 normative conventionally adopted for grading saffron, to detect the adulteration of this spice by Calendula officinalis L. petals (calendula), Carthamus tinctorius L. petals (safflower), and Curcuma longa L. powdered rhizomes (turmeric). To enhance the spectral visibility of these adulterants relative to saffron, we tested various solvents, identifying acetonitrile as the most suitable extraction medium. We analyzed 40 genuine and 123 adulterated saffron samples, each containing 5–10 % w/w contamination (41 samples for each type of adulterant), using acetonitrile extraction. The resulting UV–visible spectra were processed using unsupervised multivariate statistical methods to distinguish between authentic and adulterated saffron. The Sequential Pre-processing through Orthogonalization (SPORT) algorithm, based on sequential and orthogonalized partial least squares (SO-PLS), was first applied to differentiate the two groups. Using a calibration set of 122 samples, the SPORT model correctly classified 37 of 38 external test samples, regardless of the type or level of contamination. Additionally, a class model for genuine saffron was developed using SIMCA (Soft Independent Modelling of Class Analogies), under the same calibration and validation conditions as the SPORT model. SIMCA accurately identified all test samples, with the exception of one pure saffron and one adulterated sample.
基于乙腈提取物紫外可见光谱化学计量学处理的简便新方法,用于检测藏红花(Crocus sativus L.)中的植物源掺杂物
我们受 ISO-3632 标准的启发,开发了一种紫外可见光谱法,用于检测藏红花中掺杂的金盏花花瓣(金盏花)、红花花瓣(红花)和姜黄根茎粉(姜黄)。为了提高这些掺杂物相对于藏红花的光谱可见度,我们测试了各种溶剂,确定乙腈是最合适的萃取介质。我们使用乙腈萃取法分析了 40 个真品藏红花样品和 123 个掺假藏红花样品,每个样品都含有 5-10 % w/w 的掺假成分(每种掺假成分有 41 个样品)。所得到的紫外可见光谱采用无监督多元统计方法进行处理,以区分真假藏红花。首先应用基于序列和正交化偏最小二乘法(SO-PLS)的序列正交化预处理(SPORT)算法来区分两组藏红花。利用 122 个样本的校准集,SPORT 模型对 38 个外部测试样本中的 37 个进行了正确分类,而不论污染的类型或程度如何。除了一个纯藏红花样品和一个掺假样品外,SIMCA 能准确识别所有测试样品。
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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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