Characterization of blue cheese volatiles using fingerprinting, self-organizing maps, and entropy-based feature selection

Unveiling the dynamic response of volatile development during barley malt roasting via untargeted and pseudo-targeted flavoromics: A time course study

Roasting is an efficient way to enhance the aroma of malts. However, the dynamic response of volatile development throughout roasting has been rarely explored. In this study, multiple omics approaches were applied to systematically investigate underlying mechanisms of volatile development at a time-course manner during roasting. Roasted malts (RMs) with color ranging from 2° to 243°L were sampled at six roasting stages (RT0-5), and their free amino acids and reducing sugars were profiled and quantitated. Additionally, fatty acids (FAs) of these RMs were depicted via untargeted and targeted lipidomics, unveiling nine differentiated FAs across the six RTs. Furthermore, a comprehensive flavoromics integrating untargeted, pseudo-targeted, and targeted analyses was employed to characterize volatile development across RTs with 14 annotated compounds. Notably, non-linear patterns have been observed in malt coloration, precursors consumption, and volatile development for the first time. This work provided practical guidelines for the production and application of RMs.

Chemical and sensory analyses of cultivated pork fat tissue as a flavor enhancer for meat alternatives

The emerging field of cellular agriculture has accelerated the development of cell-cultivated adipose tissue as an additive to enhance the flavor of alternative meat products. However, there has been limited research to evaluate the sensory profile of in vitro-grown tissues compared to conventionally obtained animal fat. This study aimed to investigate the aromatic characteristics of cell-cultivated fat tissue as a flavor enhancer for meat alternatives. Porcine dedifferentiated fat (PDFAT) cells were clonally isolated and differentiated into adipocytes. This cultured adipose tissue was then analyzed alongside native porcine fat using gas chromatography-mass spectrometry (GC/MS) coupled with descriptive sensory analysis by human consumers. This evaluation enabled quantitative and qualitative assessments of volatile compounds released during cooking for both in vitro and in vivo porcine fats. The volatile profiles generated during the cooking process and fatty aroma characteristics reported by sensory consumers were largely similar between the two fat sources, with some differences in select compounds and aroma attributes. Ultimately, the consumers found comparable overall liking scores reported between the conventional and cultured porcine fats. These findings provide valuable sensory evidence supporting the viability of cell-cultivated adipose tissue as a flavor component of meat alternatives, substituting for conventional animal fat.

A comprehensive review of machine learning and its application to dairy products

Machine learning (ML) technology is a powerful tool in food science and engineering offering numerous advantages, from recognizing patterns and predicting outcomes to customizing and adjusting to individual needs. Its further development can enable researchers and industries to significantly enhance the efficiency of dairy processing while providing valuable insights into the field. This paper presents an overview of the role of machine learning in the dairy industry and its potential to improve the efficiency of dairy processing. We performed a systematic search for articles published between January 2003 and January 2023 related to machine learning in dairy products and highlighted the algorithms used. 48 studies are discussed to assist researchers in identifying the best methods that could be applied in their field and providing relevant ideas for future research directions. Moreover, a step-by-step guide to the machine learning process, including a classification of different machine learning algorithms, is provided. This review focuses on state-of-the-art machine learning applications in milk products and their transformation into other dairy products, but it also presents future perspectives and conclusions. The study serves as a valuable guide for individuals in the dairy industry interested in learning about or getting involved with ML.

Enantioseparation in high performance liquid chromatography: preparation and evaluation of a vancomycin-based chiral stationary phase via surface-initiated atom transfer radical polymerization

A chromatographic technique based on a chiral stationary phase (CSP) has been explored for enantioseparation. Herein, poly(glycidyl methacrylate) (poly(GMA)) brushes were grafted on the surface of silica gel via surface-initiated atom transfer radical polymerization (SI-ATRP), followed by the introduction of vancomycin as a chiral selector. The as-synthesized material was characterized by elemental analysis, scanning electron microscopy (SEM), Fourier transform infrared (FT-IR) and thermogravimetric analysis (TGA), proving the formation of vancomycin-immobilized brushes. Then the resulting CSP was explored to separate 7 racemic drugs (bicalutamide, 1-benzyl-5-phenylbarbituric acid, chlorpheniramine maleate, fluoxetine hydrochloride, verapamil hydrochloride, benzoxazocine hydrochloride and isoprenaline hydrochloride) in high performance liquid chromatography (HPLC). Several factors affecting the enantioseparation performance of the vancomycin-immobilized CSP, including the triethylamine (TEA) content in the buffer, pH value, content of organic solvent in the mobile phase, flow rate and injection volume, were mainly optimized. Under the optimal conditions, baseline separation of fluoxetine hydrochloride (R_S = 2.52) was achieved, which was better than that on a commercial Chirobiotic V column, while enantioseparation of bicalutamide (R_S = 1.01), chlorpheniramine maleate (R_S = 0.77), 1-benzyl-5-phenylbarbituric acid (R_S = 0.67), isoprenaline hydrochloride (R_S = 0.73), verapamil hydrochloride (R_S = 0.91) and benzoxazocine hydrochloride (R_S = 1.03) was partly achieved. It was concluded that SI-ATRP is a robust way to fabricate vancomycin-based CSPs for enantioseparation.

Key aroma compounds of Canastra cheese: HS-SPME optimization assisted by olfactometry and chemometrics

An analytical method was developed to determine volatile compounds (VC) that contribute to the aroma of cheese from Serra da Canastra (Brazil) and evaluate them in three ripening stages (fresh, short-ripened, and ripened) via headspace solid-phase microextraction (HS-SPME) combined with gas chromatography (GC). Proximate and fatty acid compositions were determined to observe whether there would be changes during ripening. Multivariate designs were applied to optimize the extraction parameters of volatile compounds and assisted by GC olfactometry (GC-O) and chemometrics. The adopted strategy revealed that the best extraction condition requires 10 min of equilibration, 75.2 min of fiber exposure at 40 °C, and 1 g of sample. The data obtained evidenced the alteration of the abundance of volatile compounds, fatty acids, and proximate composition of Canastra cheese during ripening. The fatty acid profile of the samples was mainly composed of palmitic, oleic, and stearic acids. This dairy product is rich in volatile compounds and formed primarily by alcohols (n = 14), acids (n = 13), and esters (n = 11). Olfactometry indicated that the VCs that most affected the aroma of ripened Canastra cheese were acetic acid, isobutyric acid, butanoic acid, and ethyl hexanoate. The method developed effectively discriminated against Canastra cheeses at their different ripening stages.