Journal of Food Composition and Analysis最新文献

Simultaneous estimation of Linalool and Eugenol in herbal formulations using a validated RP-HPLC method: A green analytical approach 有效的反相高效液相色谱法同时测定中药制剂中的芳樟醇和丁香酚：一种绿色分析方法

IF 4.6 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis

Pub Date : 2026-01-15 DOI: 10.1016/j.jfca.2026.108909

Ankita Das , Parixit Prajapati

Linalool and Eugenol commonly found in herbal products happen to be major bioactive components because of their anti-inflammatory, antioxidant, as well as antimicrobial activities. For the quality control and standardization of these products, a strong analytical technique that truly is effective enough is necessary. The current study develops and validates a reversed-phase high-performance liquid chromatography method that is sufficiently simple, accurate, and precise. The method quantifies combined amounts of Linalool and Eugenol in the herbal formulations. A C18 column that flowed at 1.0 mL/min separated chromatographically using acetonitrile and water mobile phase (55:45, %v/v). The approach was validated in accordance with ICH Q2(R2) guidelines for parameters like linearity, specificity, LOQ, LOD, robustness, accuracy, and precision. For both of the analytes with correlation coefficients greater than 0.9957, the technique demonstrated quite good linearity. This linearity was present between about 2–30 µg/mL. AGREE, GAPI, NEMI, tools were used to evaluate the method's greenness. Concepts of White Analytical Chemistry (WAC) were brought in using RGB fast, as well as BAGI models. Sustainability, safety, and environmental footprint, for each of the method were assessed. The method developed was successfully used in commercial herbal products, proving its applicability for routine quality control purposes.

在草药产品中常见的芳樟醇和丁香酚恰好是主要的生物活性成分，因为它们具有抗炎、抗氧化和抗菌活性。对于这些产品的质量控制和标准化，需要一个真正有效的强大的分析技术。本研究开发并验证了一种简单、准确、精确的反相高效液相色谱法。该方法定量了中药制剂中芳樟醇和丁香酚的组合量。色谱柱为C18，流速为1.0 mL/min，乙腈-水流动相（55:45,%v/v）。根据ICH Q2（R2）指南对线性、特异性、定量限、定量限、鲁棒性、准确性和精密度等参数进行了验证。对于相关系数大于0.9957的两种分析物，该技术表现出良好的线性。线性关系在2-30 µg/mL之间。采用AGREE、GAPI、NEMI等工具评价方法的绿色度。白色分析化学（WAC）的概念被引入使用RGB快速，以及BAGI模型。对每种方法的可持续性、安全性和环境足迹进行了评估。所建立的方法已成功地应用于商业草药产品中，证明了其在日常质量控制中的适用性。

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引用次数: 0

Yellow pea germination impact on protein isolation - protein digestibility and quality 黄豌豆发芽对蛋白质分离-蛋白质消化率和品质的影响

IF 4.6 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis

Pub Date : 2026-01-15 DOI: 10.1016/j.jfca.2026.108902

Anamaria Pop , Călina Ciont , Oana-Leila Pop , Dan-Cristian Vodnar , Răzvan Odocheanu , Elena Mudura , Georgiana Smaranda Marțiș , Sonia Ancuța Socaci , Gheorghe-Adrian Martău

Enhancing the amino acid composition and nutritional quality of plant-based protein ingredients is essential for sustainable food development. This study investigated the effects of germination on yellow pea (Pisum sativum L.) protein isolates using the micellar precipitation method. The results indicate that short-term germination induces compositional and structural modifications in yellow pea proteins, which persist through micellar precipitation and influence the amino acid profile of the resulting isolates. Germination activated endogenous proteolytic systems, leading to partial hydrolysis of storage globulins and a shift toward lower-molecular-weight peptides. Germinated isolates exhibited a lower loss of essential and non-essential amino acids during digestion, resulting in higher non-essential amino acid bioaccessibility (23 %) compared with non-germinated isolates (17 %). After digestion, germinated isolates showed reduced histidine and phenylalanine levels, reflecting increased digestive release of these amino acids. Glutamic acid content increased in germinated isolates compared to non-germinated ones, while glycine levels rose post-digestion in germinated samples, indicating improved bioaccessibility. These findings underscore the value of germination as a strategy to improve the amino acid composition and digestive bioaccessibility of pea protein isolates, reinforcing their nutritional relevance for plant-based protein applications.

提高植物性蛋白质成分的氨基酸组成和营养质量对可持续粮食发展至关重要。采用胶束沉淀法研究了黄豌豆（Pisum sativum L.）蛋白分离物萌发的影响。结果表明，短期萌发诱导黄豌豆蛋白的组成和结构变化，这些变化通过胶束沉淀持续存在，并影响分离物的氨基酸谱。发芽激活了内源性蛋白水解系统，导致储存球蛋白部分水解，并向低分子量肽转移。发芽菌株在消化过程中必需和非必需氨基酸的损失更低，与未发芽菌株（17 %）相比，非必需氨基酸的生物可及性更高（23 %）。消化后，发芽的分离物显示组氨酸和苯丙氨酸水平降低，反映了这些氨基酸的消化释放增加。与未萌发的菌株相比，萌发后的菌株谷氨酸含量增加，而消化后的菌株甘氨酸水平上升，表明生物可及性提高。这些发现强调了发芽作为一种改善豌豆分离蛋白氨基酸组成和消化生物可及性的策略的价值，加强了它们在植物蛋白应用中的营养相关性。

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引用次数: 0

A FRET-triggered probe for specific and interference-resistant detection of organophosphorus pesticides by composite Zr-MOFs 复合Zr-MOFs的fret触发探针特异性和抗干扰检测有机磷农药

IF 4.6 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis

Pub Date : 2026-01-14 DOI: 10.1016/j.jfca.2026.108901

Youjia Tian , Zhijie Wang , Mengyao Li , Xianwei Luo , Mingxin Yang , Jingru Yang , Bingbing Peng , Jinhong Jiang , Minjie Li , Jia Liu , Dingyu Hu , Juan Li

Organophosphorus pesticides (OPPs) posed significant risks to both ecological and human health due to their widespread use and toxicity. Traditional detection methods for OPPs are complex and require expensive equipment. In this study, a novel fluorescence-based detection strategy is proposed, using serine-based carbon quantum dots (CQDs) encapsulated in a Zr-based porphyrin metal-organic framework (MOF). This composite material exploits the triggering of fluorescence resonance energy transfer (FRET) between the CQDs and Zr-MOF for selective detection of OPPs. The FRET efficiency of this system is 72.9 %, enabling it to have interference-resistant detection capabilities. The detection limits are approaching the detection limit of gas chromatography (∼5 µg/L). The probe is applicable for environmental monitoring and food safety testing, offering a promising alternative for rapid on-site detection of OPPs without relying on enzymes.

有机磷农药由于其广泛使用和毒性，对生态和人类健康构成重大风险。传统的opp检测方法复杂且需要昂贵的设备。在这项研究中，提出了一种新的基于荧光的检测策略，使用丝氨酸基碳量子点（CQDs）封装在基于zr的卟啉金属有机框架（MOF）中。该复合材料利用CQDs和Zr-MOF之间的荧光共振能量转移（FRET）触发选择性检测opp。该系统的FRET效率为72.9 %，具有抗干扰检测能力。检出限接近气相色谱法的检出限（~ 5 µg/L）。该探针适用于环境监测和食品安全检测，为不依赖于酶的OPPs快速现场检测提供了一种有前景的替代方案。

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引用次数: 0

Effects of sulfuring, drying and storage on the formation of α-dicarbonyl compounds and 5-hydroxymethylfurfural in dried apricots 硫化、干燥和贮藏对杏干中α-二羰基化合物和5-羟甲基糠醛形成的影响

IF 4.6 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis

Pub Date : 2026-01-14 DOI: 10.1016/j.jfca.2026.108904

Işıl Gürsul Aktağ , Ezgi Doğan Cömert , Vural Gökmen

This study investigated the impact of sulfuring, various drying methods, and storage with different packaging on the formation of α-dicarbonyl compounds and 5-hydroxymethylfurfural in dried apricots. Changes in the concentrations of α-dicarbonyl compounds, 5-hydroxymethylfurfural, sugars, and color were monitored in the samples (sulfured/non-sulfured, oven-/sun-dried, polyethylene/vacuum/modified atmosphere packaging) during storage at room temperature for 12 months. Among α-dicarbonyl compounds, 3-deoxyglucosone was the most prevalent, reaching 1356.8 ± 70.6 mg/kg dry matter in sulfured oven-dried apricots after drying. Methylglyoxal reached its peak at 723.2 ± 9.9 mg/kg dry matter in sulfured sun-dried apricots after storage. 5-Hydroxymethylfurfural remained at significantly lower levels than the dominant α-dicarbonyl compounds. Both α-dicarbonyl compounds and 5-hydroxymethylfurfural increased significantly during storage, irrespective of packaging, while sulfuring influenced the type of α-dicarbonyl compounds formed. These findings underscore the significance of processing and storage in influencing α-dicarbonyl compounds accumulation, providing valuable insights into maintaining the safety and quality of dried apricots.

研究了硫化、不同干燥方式和不同包装对杏脯中α-二羰基化合物和5-羟甲基糠醛形成的影响。在室温下存放12个月的样品（含硫/无硫、烘箱/晒干、聚乙烯/真空/改性气氛包装）中，监测α-二羰基化合物、5-羟甲基糠醛、糖和颜色的浓度变化。在α-二羰基化合物中，以3-脱氧葡萄糖酮含量最高，干燥后硫化杏干物质含量达到1356.8 ± 70.6 mg/kg。含硫晒干杏中甲基乙二醛含量最高，为723.2 ± 9.9 mg/kg。5-羟甲基糠醛的含量明显低于α-二羰基化合物。无论包装如何，α-二羰基化合物和5-羟甲基糠醛在储存过程中均显著增加，而硫化影响α-二羰基化合物形成的类型。这些发现强调了加工和储存对α-二羰基化合物积累的影响，为保持杏干的安全和质量提供了有价值的见解。

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引用次数: 0

Comparison of NIR spectroscopy devices (Portable and Benchtop) and a low-cost E-nose for classification of black tea: A machine learning approach using PLSDA, LDA, and PCA 近红外光谱设备（便携式和台式）与低成本电子鼻用于红茶分类的比较：使用PLSDA， LDA和PCA的机器学习方法

IF 4.6 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis

Pub Date : 2026-01-14 DOI: 10.1016/j.jfca.2026.108898

Marcus Vinicius da Silva Ferreira , Jose Lucena Barbosa Jr , Douglas Fernandes Barbin , Mohammed kamruzzaman

Black tea adulteration is a widespread practice, involving the mixing or substitution of high-quality tea leaves with lower quality samples. The authentication of black tea regarding its composition and origin is crucial to ensure quality and maintain consumer trust. However, conventional methods (e.g., High-Performance Liquid Chromatography HPLC) for tea analysis are expensive, demand chemicals, and generate waste. This study evaluates two NIR instruments (portable (PNIR) and benchtop (BNIR)) and low-cost electronic nose (LC-e-nose) for classification of tea leaves from three different origins: Brazil (BR), United States (US), and India (IND). Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Partial Least Squares Discriminant Analysis (PLS-DA) were applied for data treatment, and the results demonstrate that the LC-e-nose exhibits F1 scores above 99 % for all models, comparable to NIR systems (F1 > 99 %). These findings highlight its potential as a cost-effective and reliable alternative to NIR spectroscopy for investigating tea quality and origin. By comparing these advanced techniques, this study provides an evaluation of black tea quality and authenticity at a significantly reduced cost.

红茶掺假是一种普遍的做法，涉及用低质量的样品混合或替代高质量的茶叶。红茶的成分和原产地认证对于确保质量和保持消费者的信任至关重要。然而，传统的茶叶分析方法（如高效液相色谱法）价格昂贵，需要化学物质，而且会产生废物。本研究评估了两种近红外仪器（便携式（PNIR）和台式（BNIR））和低成本电子鼻（lc -e-鼻）用于三个不同产地的茶叶分类：巴西（BR），美国（US）和印度（IND）。采用主成分分析（PCA）、线性判别分析（LDA）和偏最小二乘判别分析（PLS-DA）进行数据处理，结果表明，LC-e-nose在所有模型上的F1得分都在99 %以上，与NIR系统（F1 > 99 %）相当。这些发现突出了它作为一种具有成本效益和可靠的替代近红外光谱研究茶叶质量和原产地的潜力。通过对这些先进技术的比较，本研究以显著降低的成本提供了红茶质量和真实性的评估。

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引用次数: 0

Non-destructive detection of internal mold in Torreya grandis seeds using NIR spectroscopy and deep learning with integrated preprocessing and variable selection 综合预处理和变量选择的近红外光谱和深度学习技术在香榧种子内部霉菌无损检测中的应用

IF 4.6 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis

Pub Date : 2026-01-12 DOI: 10.1016/j.jfca.2026.108899

Yuan Du , Haihang Wang , Binyan Hou , Jiajun Zan , Jiasheng Wu , Tong Sun , Lili Song

Torreya grandis seeds are valued for their nutritional and culinary benefits, but internal mold can affect seed quality without being visible. This study explores the use of near-infrared spectroscopy for rapid, non-destructive detection of internal mold in these seeds. By analyzing spectral differences between healthy and moldy seeds, various preprocessing techniques were applied to optimize the data. Key wavelengths were selected using both individual and hybrid variable selection methods. Linear discriminant analysis (LDA), convolutional neural networks (CNN), and multilayer perceptron (MLP) models were developed for classification. Shapley additive explanations (SHAP) visualized the impact of key wavelengths, while t-distributed Stochastic Neighbor Embedding (t-SNE) was used for model visualization. The results show that CNN and MLP models outperform the traditional LDA model. The Savitzky-Golay (SG)-Baseline-Single Nucleotide Variant (SNV) preprocessing method is the most effective for CNN and MLP models. Competitive Adaptive Reweighted Sampling (CARS)-Iterative Channel Optimization (ICO) and Univariate Variable Elimination (UVE)-CARS-ICO were optimal for variable selection in CNN and MLP, respectively. The accuracy, sensitivity, and specificity of the CARS-ICO-CNN and UVE-CARS-ICO-MLP models were 97.22 %, 96.15 %, 97.83 % and 97.22 %, 92.31 %, 100 %, respectively. These findings highlight the potential of NIR spectroscopy and deep learning models for quality control in seed processing.

香榧种子因其营养和烹饪价值而受到重视，但内部霉菌会在不可见的情况下影响种子质量。本研究探索了使用近红外光谱快速、无损地检测这些种子中的内部霉菌。通过分析健康种子和霉变种子的光谱差异，采用多种预处理技术对数据进行优化。关键波长的选择采用单独和混合变量选择方法。采用线性判别分析（LDA）、卷积神经网络（CNN）和多层感知器（MLP）模型进行分类。Shapley加性解释（SHAP）将关键波长的影响可视化，t分布随机近邻嵌入（t-SNE）则用于模型可视化。结果表明，CNN和MLP模型优于传统的LDA模型。Savitzky-Golay (SG)-Baseline-Single Nucleotide Variant （SNV）预处理方法对CNN和MLP模型最有效。竞争自适应重加权抽样(CARS)-迭代信道优化（ICO）和单变量变量消除(UVE)-CARS-ICO分别是CNN和MLP中变量选择的最优方法。CARS-ICO-CNN和UVE-CARS-ICO-MLP模型的准确率、灵敏度和特异性分别为97.22 %、96.15 %、97.83 %和97.22 %、92.31 %、100 %。这些发现突出了近红外光谱和深度学习模型在种子加工质量控制方面的潜力。

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引用次数: 0

Validation of benchtop NMR-based quantification of saturated, monounsaturated, and polyunsaturated fatty acids in edible oils 基于台式核磁共振的食用油饱和、单不饱和和多不饱和脂肪酸定量方法的验证

IF 4.6 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis

Pub Date : 2026-01-12 DOI: 10.1016/j.jfca.2026.108897

Umrbek Mavlanov , Sardorbek Atajanov , Sharofiddin Nuriddinov , Sardorjon Shukurov Salimovich , Soyibjon Bozorov , Tomasz Paweł Czaja , Bekzod Khakimov

Proton nuclear magnetic resonance (¹H NMR) spectroscopy offers rapid quantification of saturated (SFA), monounsaturated (MUFA), and polyunsaturated (PUFA) fatty acids in oils. While high-field NMR has been widely applied, its high operational costs limit accessibility. In contrast, benchtop NMR provides an affordable and user-friendly option for oil analysis. However, its lower sensitivity and resolution have led methods to rely on chemometric modeling, requiring spectra acquired under identical conditions as the reference model, thereby restricting applicability. In this study, we validated for the first time a benchtop (80 MHz) ¹H NMR method for direct quantification of SFA, MUFA, and PUFA in oils using integration of characteristic resonances, enabling a model-free and reproducible approach. To our knowledge, this is the first benchtop NMR validation of SFA, MUFA, and PUFA resonances using authentic triglyceride standards, glyceryl tridecanoate, glyceryl trioleate, and glyceryl trilinoleate, respectively. Binary and ternary mixtures, natural oil blends, and spiked sunflower oils prepared from these standards enabled the direct comparison of fatty acid quantification between benchtop and high-field (500 MHz) ¹H NMR. Our optimized workflow enabled both platforms to deliver comparable accuracy and reproducibility. This validated, model-free approach provides reliable fatty acid profiling, accelerating oil analysis in research and quality control laboratories.

质子核磁共振（1H NMR）光谱提供了油中饱和（SFA），单不饱和（MUFA）和多不饱和（PUFA）脂肪酸的快速定量。虽然高场核磁共振得到了广泛的应用，但其高昂的运行成本限制了其可及性。相比之下，台式核磁共振为油分析提供了一种经济实惠且用户友好的选择。然而，其较低的灵敏度和分辨率导致方法依赖于化学计量学建模，需要在与参考模型相同的条件下获得光谱，从而限制了适用性。在这项研究中，我们首次验证了台式（80 MHz） 1H NMR方法，该方法使用特征共振集成直接定量油中的SFA， MUFA和PUFA，从而实现了无模型和可重复的方法。据我们所知，这是SFA、MUFA和PUFA共振的第一次台式核磁共振验证，分别使用正宗的甘油三酯标准，三酸甘油酯、三油酸甘油三酯和三油酸甘油三酯。根据这些标准制备的二元和三元混合物，天然油混合物和加标向日葵油可以直接比较台式和高场（500 MHz） 1H NMR之间的脂肪酸定量。我们优化的工作流程使两个平台能够提供相当的准确性和再现性。这种经过验证的无模型方法提供了可靠的脂肪酸分析，加速了研究和质量控制实验室的油分析。

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引用次数: 0

Interpretable machine learning for origin classification of brazilian soybeans: A random forest and XAI-based approach 巴西大豆原产地分类的可解释机器学习：随机森林和基于xai的方法

IF 4.6 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis

Pub Date : 2026-01-12 DOI: 10.1016/j.jfca.2026.108896

Khushboo Soni , Russell Frew , Biniam Kebede

eXplainable Artificial Intelligence (XAI) offers a powerful framework for enhancing the transparency and trustworthiness of machine learning models in highly regulated fields such as food traceability. In this study, we applied XAI techniques, SHAP (SHapley Additive exPlanations) and Partial Dependence Plots (PDPs), to interpret a Random Forest (RF) classification model developed to determine the geographical origin of Brazilian soybean samples. A total of 60 samples, representing two biomes and six states, were analysed using stable isotope ratios (δ¹³C, δ¹⁵N, δ²H, δ¹⁸O, δ³⁴S) and elemental composition (41 elements). The RF model achieved high classification accuracy at both the biome and state levels using the fused stable isotopes and elemental composition datasets. XAI tools revealed δ¹⁸O, δ²H, Rb, Cs, and Ca as the most influential features, with δ¹⁸O consistently emerging as the dominant predictor. SHAP beeswarm and waterfall plots provided global and local explanations of feature importance, while PDPs and two-way PDPs captured non-linear relationships and synergistic effects between isotopic and elemental variables. These findings confirm the discriminative power of geochemical markers and show the practical value of interpretable models for agroecological traceability and regulatory compliance. This approach advances XAI in food provenance, providing a transparent, region-specific framework that supports sustainability initiatives.

可解释人工智能（XAI）为在食品可追溯性等高度监管领域提高机器学习模型的透明度和可信度提供了一个强大的框架。在这项研究中，我们应用XAI技术、SHapley加性解释（SHapley Additive explanation）和部分依赖图（pdp）来解释为确定巴西大豆样本地理来源而建立的随机森林（Random Forest）分类模型。采用稳定同位素比值（δ13C、δ15N、δ2H、δ18O、δ34S）和元素组成（41个元素）对60个样品进行了分析。利用融合稳定同位素和元素组成数据集，RF模型在生物群系和状态水平上都取得了较高的分类精度。XAI工具显示δ18O、δ2H、Rb、Cs和Ca是影响最大的特征，δ18O一直是主要的预测因子。SHAP蜂群图和瀑布图提供了特征重要性的全局和局部解释，而pdp图和双向pdp图则捕获了同位素和元素变量之间的非线性关系和协同效应。这些发现证实了地球化学标记的鉴别能力，并显示了可解释模型在农业生态可追溯性和法规遵从性方面的实用价值。这种方法促进了XAI在食物来源方面的发展，提供了一个透明的、特定于区域的框架，支持可持续性倡议。

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引用次数: 0

Dutch Total Diet Study: Description and dietary intake of protein and fat of 3–11-year-old children 荷兰总饮食研究：3 - 11岁儿童蛋白质和脂肪摄入量的描述

IF 4.6 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis

Pub Date : 2026-01-12 DOI: 10.1016/j.jfca.2026.108894

Leontien de Pagter-de Witte , Jacqueline J.M. Castenmiller , Jan Dirk te Biesebeek , Annemieke M. Pustjens , Polly E. Boon

A Total Diet Study (TDS) of 3–5-, 6–8- and 9–11-year-old children has been conducted, based on food consumption data of the Dutch National Food Consumption Survey (DNFCS) 2019–2021. In total, 2258 foods, including beverages were bought, prepared if applicable, and pooled into 195 composite samples. Protein and fat concentrations of the composite samples were analysed and the intake per age group was calculated. For children aged 3–5 years, 6–8 years and 9–11 years, mean protein intakes were 47, 56 and 65 g/day, respectively with main contributions from “bread” and “milk and milk-based beverages”. The mean fat intakes were 56, 65 and 77 g/day, respectively. Consumption of “chocolates", "margarines and butter” and “oils and fats" contributed most to the fat intake. These intakes are higher than the reference intakes from the Health Council of the Netherlands for protein and close to the maximum intake values for fat. Protein and fat intake estimates based on concentrations of analysed foods, including beverages, from the present TDS were comparable to those obtained using the Dutch food composition database (NEVO).

根据荷兰国家食品消费调查（DNFCS） 2019-2021年的食品消费数据，对3-5岁、6-8岁和9 - 11岁儿童进行了一项总饮食研究（TDS）。总共购买了2258种食品（包括饮料），并在适用的情况下进行了配制，并汇集成195种复合样品。分析了复合样品的蛋白质和脂肪浓度，并计算了每个年龄组的摄入量。对于3-5岁、6-8岁和9-11岁的儿童，平均蛋白质摄入量分别为47、56和65 g/天，主要来自“面包”和“牛奶及牛奶饮料”。平均脂肪摄入量分别为56、65和77 克/天。食用“巧克力”、“人造黄油和黄油”以及“油脂”是脂肪摄入的主要原因。这些摄入量高于荷兰卫生委员会提供的蛋白质参考摄入量，接近脂肪的最大摄入量。目前TDS中基于分析食品（包括饮料）浓度的蛋白质和脂肪摄入量估计与使用荷兰食品成分数据库（NEVO）获得的结果相当。

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引用次数: 0

Impact of geographical origin, vintage and plant material on the phenolic and sensory characteristics of Cabernet Sauvignon wines from Mendoza, Argentina 产地、年份和植物材料对阿根廷门多萨赤霞珠葡萄酒酚性和感官特性的影响

IF 4.6 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis

Pub Date : 2026-01-10 DOI: 10.1016/j.jfca.2026.108895

Flavio Muñoz , Roy Urvieta , Aníbal Catania , Fernando Buscema , Ariel Fontana , Federico Berli

Geographical origin, vintage and plant material shape chemical and sensory typicity of Cabernet Sauvignon, yet their relative contributions across sites and years remain insufficiently quantified. We assessed four Geographical Indications (GIs) (Agrelo, Altamira, Pampa El Cepillo and Gualtallary), three vintages (2018, 2019 and 2022) and two Cabernet Sauvignon clonal families Clone 7 (C7) and Mount Eden (ME). Triplicate micro-vinifications per GI × plant material × vintage under identical conditions, targeted LC-DAD-FLD phenolic profiling with chemometrics (ANOVA/MANOVA, MFA, clustering), and trained-panel descriptive sensory analysis in 2019 and 2022 were evaluated. Geographical origin accounted for the largest multivariate separation, with Gualtallary (1350 m a.s.l.) consistently discriminated across vintages. High-altitude wines showed higher di-hydroxylated anthocyanins, quercetin and trans-resveratrol together with lower pH and higher total acidity than other GIs; vintage modulated absolute concentrations (warmer 2022 vs. cooler 2018 and 2019), but GI patterns persisted. The plant-material effect was comparatively weak for GI discrimination, and both materials from Gualtallary grouped apart from other origins. Sensory profiles differed by GI in both panels, supporting the chemical separation. Within the bounds of this observational, multi-vintage design, origin (modulated by altitude-related environments) emerged as a robust structuring factor of Mendoza Cabernet Sauvignon typicity, and the results provide candidate GI level markers and a framework for multi-vintage typicity assessments.

地理来源、年份和植物材料塑造了赤霞珠的化学和感官典型性，但它们在不同地点和年份之间的相对贡献尚未得到充分的量化。我们评估了四个地理标志（Agrelo, Altamira， Pampa El Cepillo和Gualtallary），三个年份（2018,2019和2022）和两个赤霞珠克隆家族克隆7号（C7）和伊甸山（ME）。采用化学计量学（ANOVA/MANOVA、MFA、聚类）和训练面板描述性感官分析，对2019年和2022年每GI ×植物材料×葡萄年份的三次微发酵进行了评估。地理来源占最大的多变量分离，高tallary（1350 m a.s.l）在不同年份中一致区分。高原葡萄酒的二羟基花青素、槲皮素和反式白藜芦醇含量较高，pH值较低，总酸度较高；年份调节了绝对浓度（2022年较暖，2018年和2019年较冷），但GI模式持续存在。植物材料效应对GI区分的影响相对较弱，来自高tallary的两种材料与其他来源的材料分开分组。两个面板的感觉剖面因GI不同而不同，这支持了化学分离。在这一观察范围内，多年份设计、原产地（受海拔相关环境的调节）成为门多萨赤霞珠典型性的一个强有力的结构因素，研究结果为多年份典型性评估提供了候选GI水平标记和框架。

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