Monitoring of dough fermentation during Chinese steamed bread processing by near‐infrared spectroscopy combined with spectra selection and supervised learning algorithm

Rapid non-destructive monitoring and quality assessment of the fumigation process of Shanxi aged vinegar based on Vis-NIR hyperspectral imaging combined with multiple chemometric algorithms

The quality of the grains during the fumigation process can significantly affect the flavour and nutritional value of Shanxi aged vinegar (SAV). Hyperspectral imaging (HSI) was used to monitor the extent of fumigated grains, and it was combined with chemometrics to quantitatively predict three key physicochemical constituents: moisture content (MC), total acid (TA) and amino acid nitrogen (AAN). The noise reduction effects of five spectral preprocessing methods were compared, followed by the screening of optimal wavelengths using competitive adaptive reweighted sampling. Support vector machine classification was employed to establish a model for discriminating fumigated grains, and the best recognition accuracy reached 100%. Furthermore, the results of partial least squares regression slightly outperformed support vector machine regression, with correlation coefficient for prediction (Rp) of 0.9697, 0.9716, and 0.9098 for MC, TA, and AAN, respectively. The study demonstrates that HSI can be employed for rapid non-destructive monitoring and quality assessment of the fumigation process in SAV.

Effects of Wickerhamomyces anomalus Co-Fermented with Saccharomyces cerevisiae on Volatile Flavor Profiles during Steamed Bread Making Using Electronic Nose and HS-SPME-GC-MS

Steamed bread is a traditional staple food in China, and it has gradually become loved by people all over the world because of its healthy production methods. With the improvement in people's living standards, the light flavor of steamed bread fermented by single yeast cannot meet people's needs. Multi-strain co-fermentation is a feasible way to improve the flavor of steamed bread. Here, the dynamic change profiles of volatile substances in steamed bread co-fermented by Saccharomyces cerevisiae SQJ20 and Wickerhamomyces anomalus GZJ2 were analyzed using the electronic nose (E-nose) and headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS). The five detectors of the E-nose rapidly detected the changes in volatile substances in different dough or steamed bread with the highest response value in co-fermented dough. A total of 236 volatile substances were detected in all the samples using HS-SPME-GC-MS, and alcohols were the most variable component, especially Phenylethyl alcohol. Significantly, more alcohols and esters were upregulated in co-fermented dough, and the addition of W. anomalus GZJ2 improved the key volatile aroma compounds of steamed bread using the relative odor activity value method (ROAV), especially the aldehydes and alcohols. Moreover, these key volatile aroma compounds can be quickly distinguished using the W2S detector of the E-nose, which can be used for the rapid detection of aroma components in steamed bread.

Innovations in nondestructive assessment of baked products: Current trends and future prospects

Rising consumer awareness, coupled with advances in sensor technology, is propelling the food manufacturing industry to innovate and employ tools that ensure the production of safe, nutritious, and environmentally sustainable products. Amidst a plethora of nondestructive techniques available for evaluating the quality attributes of both raw and processed foods, the challenge lies in determining the most fitting solution for diverse products, given that each method possesses its unique strengths and limitations. This comprehensive review focuses on baked goods, wherein we delve into recently published literature on cutting-edge nondestructive methods to assess their feasibility for Industry 4.0 implementation. Emphasizing the need for quality control modalities that align with consumer expectations regarding sensory traits such as texture, flavor, appearance, and nutritional content, the review explores an array of advanced methodologies, including hyperspectral imaging, magnetic resonance imaging, terahertz, acoustics, ultrasound, X-ray systems, and infrared spectroscopy. By elucidating the principles, applications, and impacts of these techniques on the quality of baked goods, the review provides a thorough synthesis of the most current published studies and industry practices. It highlights how these methodologies enable defect detection, nutritional content prediction, texture evaluation, shelf-life forecasting, and real-time monitoring of baking processes. Additionally, the review addresses the inherent challenges these nondestructive techniques face, ranging from cost considerations to calibration, standardization, and the industry's overreliance on big data.