利用傅立叶变换近红外光谱和 PLS 回归比较不同光谱范围对红葡萄汁酒精发酵和醋酸发酵的监控作用

IF 2.6 3区 农林科学 Q2 FOOD SCIENCE & TECHNOLOGY
Camilla Menozzi, Giorgia Foca, Rosalba Calvini, Lisa Catellani, Andrea Bezzecchi, Alessandro Ulrici
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引用次数: 0

摘要

葡萄酒醋是通过葡萄汁的两阶段发酵生产的:首先,酵母将葡萄糖转化为乙醇,然后,醋酸菌将乙醇氧化为醋酸。如果采用表面发酵法,这一过程会持续数周,因此需要持续监控乙醇和总酸度水平。为了提高过程监控的质量和效率,食醋生产正在转向更快、环境可持续的方法。近红外(NIR)光谱因其非侵入性和快速性而得到认可,是在线实施过程控制的理想选择。本研究对红葡萄汁的双重发酵进行了长期跟踪,同时监测两个不同批次的发酵过程,以评估发酵动力学和可重复性。在整个发酵过程中,使用传统的实验室分析和傅立叶变换近红外光谱分析发酵葡萄汁中的乙醇含量和总酸度。使用主成分分析法(PCA)来探索光谱数据集,然后使用偏最小二乘法(PLS)来建立预测乙醇和酸度的校准模型。将整个光谱范围计算出的模型与两个较窄区域计算出的模型进行了比较,在这两个较窄区域,市场上出现了成本效益更高、更易于微型化的传感器。无论是在整个光谱范围(12500-4000 cm-1,相当于 800-2500 nm)还是在 10526-6060 cm-1 (950-1650 nm) 区域,傅立叶变换近红外光谱都能有效测定乙醇含量和酸度(R2Pred > 0.98)。在 12,500-9346 cm-1(800-1070 nm)区域(R2Pred >0.81),虽然结果不尽如人意,但仍可接受,这证实了成本效益型设备在实时发酵监测方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Comparison of Different Spectral Ranges to Monitor Alcoholic and Acetic Fermentation of Red Grape Must Using FT-NIR Spectroscopy and PLS Regression

Comparison of Different Spectral Ranges to Monitor Alcoholic and Acetic Fermentation of Red Grape Must Using FT-NIR Spectroscopy and PLS Regression

Wine vinegar is produced through a two-phase fermentation of grape must: initially, yeast converts grape sugars into ethanol, and subsequently, acetobacteria oxidize ethanol into acetic acid. This process, spanning weeks when conducted by surface fermentation, requires constant monitoring of ethanol and total acidity levels. To enhance the quality and efficiency of process monitoring, vinegar production is shifting to faster, environmentally sustainable methods. Near-infrared (NIR) spectroscopy, recognized for its non-invasiveness and speed, is ideal for online implementation in process control. This study tracked dual fermentation in red grape must over an extended period, monitoring two different batches simultaneously to assess fermentation kinetics and reproducibility. Ethanol content and total acidity were analyzed in fermenting musts throughout the whole fermentation process using both classical laboratory analyses and FT-NIR spectroscopy. Principal Component Analysis (PCA) was used to explore the spectral dataset, then Partial Least Squares (PLS) was used to develop calibration models for predicting ethanol and acidity. The models calculated considering the entire spectral range were compared with those obtained for two narrower zones, where more cost-effective and easily miniaturizable sensors are available on the market. FT-NIR allowed to effectively determine ethanol content and acidity (R2Pred > 0.98), both over the entire range (12,500–4000 cm−1, corresponding to 800–2500 nm) and in the 10,526–6060 cm−1 (950–1650 nm) region. Although less satisfactory, still acceptable results were obtained in the 12,500–9346 cm−1 (800–1070 nm) region (R2Pred > 0.81), confirming the potential for cost-effective devices in real-time fermentation monitoring.

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来源期刊
Food Analytical Methods
Food Analytical Methods 农林科学-食品科技
CiteScore
6.00
自引率
3.40%
发文量
244
审稿时长
3.1 months
期刊介绍: Food Analytical Methods publishes original articles, review articles, and notes on novel and/or state-of-the-art analytical methods or issues to be solved, as well as significant improvements or interesting applications to existing methods. These include analytical technology and methodology for food microbial contaminants, food chemistry and toxicology, food quality, food authenticity and food traceability. The journal covers fundamental and specific aspects of the development, optimization, and practical implementation in routine laboratories, and validation of food analytical methods for the monitoring of food safety and quality.
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