Influence of storage time and elevated ripening temperature on the chemical and sensory properties of white Cheddar cheese

Biochemical and chemosensory characterization of doubanjiang fermented via two-stage controlled temperature

This study aimed to elaborate the biochemical and chemosensory characteristics of doubanjiang fermentation via a two-stage controlled temperature. HL group with variable temperature (40 °C → 30 °C) and NTF group fermented following traditional technique were prepared and their volatile and non-volatile metabolites were compared through multivariate statistical analysis. HL group favored the accumulation of amino acid nitrogen, free amino acids and organic acids in the early stage and maintained adequate total acids and biogenic amines in the mid-late stage. HL group also had preferred jiang and fruity flavor through sensory evaluation. A total of 116 volatile metabolites were identified in HL or NTF groups and 22 marker volatile metabolites were screened through the combinational use of OPLS-DA and Random Forest analysis. Stronger anti-oxidant ability was observed in HL group while adequate number of acidic compounds and biogenic amines were ensured. This indicated that the two-stage controlled temperature fermentation was beneficial for doubanjiang fermentation.

Preparation of pH-responsive chitosan microspheres containing aminopeptidase and their application in accelerating cheese ripening

Microencapsulated enzymes have been found to effectively accelerate cheese ripening. However, microencapsulated enzyme release is difficult to control, often resulting in enzyme release during cheese processing and causing texture and flavor defects. This study aims to address this issue by developing aminopeptidase-loaded pH-responsive chitosan microspheres (A-CM) for precise enzyme release during cheese ripening. An aminopeptidase with an isoelectric point (pH 5.4) close to the pH value of cheese ripening was loaded on chitosan microspheres through electrostatic interaction. Turbidity titration measurements revealed that pH 6.5 was optimal for binding aminopeptidase and microspheres, affording the highest loading efficiency of 58.16%. Various characterization techniques, including scanning electron microscopy, energy-dispersive X-ray spectroscopy, and Fourier-transform infrared spectroscopy confirmed the successful loading of aminopeptidase molecules on the chitosan microspheres. In vitro release experiments conducted during simulated cheese production demonstrated that aminopeptidase release from A-CM was pH responsive. The microspheres retained the enzyme during the coagulation and cheddaring processes (pH 5.5-6.5) and only released it after entering the cheese-ripening stage (pH 5.0-5.5). By loading aminopeptidase on chitosan microspheres, the loss rate of the enzyme in cheese whey was reduced by approximately 79%. Furthermore, compared with cheese without aminopeptidase and cheese with aminopeptidase added directly, the cheeses made with A-CM exhibited the highest proteolysis level and received superior sensory ratings for taste and smell. The content of key aroma substances, such as 2/3-methylbutanal and ethyl butyrate, in cheese with A-CM was more than 15 times higher than the others. This study provides an approach for accelerating cheese ripening through the use of microencapsulated enzymes.

Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review

A greater demand for high-quality food is being driven by the growth of economic and technological advancements. In this context, consumers are currently paying special attention to organoleptic characteristics such as smell, taste, and appearance. Motivated to mimic human senses, scientists developed electronic devices such as e-noses, e-tongues, and e-eyes, to spot signals relative to different chemical substances prevalent in food systems. To interpret the information provided by the sensors' responses, multiple chemometric approaches are used depending on the aim of the study. This review based on the Web of Science database, endeavored to scrutinize three e-sensing systems coupled to chemometric approaches for food quality evaluation. A total of 122 eligible articles pertaining to the e-nose, e-tongue and e-eye devices were selected to conduct this review. Most of the performed studies used exploratory analysis based on linear factorial methods, while classification and regression techniques came in the second position. Although their applications have been less common in food science, it is to be noted that nonlinear approaches based on artificial intelligence and machine learning deployed in a big-data context have generally yielded better results for classification and regression purposes, providing new perspectives for future studies.

The study of nutritional value and microbiological characteristics of brine cheese with vegetable additive

This article investigated brine cheeses' nutritional value and safety by adding vegetable additives (dry powder of white cabbage and coriander). Brynza brine cheese was used as the basis for the recipe. By the chemical composition of the cheese with vegetable, additives has a significantly higher protein content (26.27 g/100g), while the fat content is lower (14.98 g/100g). There is a high content of amino acids and fatty acids (PUFA 6%, MUFA 24%). During prolonged storage of brine cheese, water activity aw decreases in control from 0.997 to 0.990, mass fraction of moisture increases from 60% to 62.5%, in the brine cheese with vegetable additives aw from 0.998 to 0.991, mass fraction of moisture from 61.1% to 63.7%.The use of vegetable additives in the formulation of cheeses does not affect the deterioration of microbiological parameters compared to the control sample. As a result of experimental studies, the shelf life of brine cheese with vegetable additives is 8-10 days.

The Aroma Composition of Koryciński Cheese Ripened in Different Temperatures

As a regional product, Koryciński cheese is one of the most important cheeses in the Podlasie region of Poland. In this study, the influence of technological processes, such as ripening, on shaping the organoleptic characteristics of cheese was determined. Korycin-type cheeses are produced from cow's milk according to traditional technology. The ripening process lasted 466 h at 5 °C, 10 °C, and 15 °C. The aging temperature had a decisive influence on the number of esters and organic acids formed, which were analyzed by gas chromatography-mass spectrometry (GC/MS). The organoleptic properties of the cheeses were also related to the ripening temperature.

Detection of Red Wine Faults over Time with Flash Profiling and the Electronic Tongue

Wine faults, often caused by spoilage microorganisms, are considered negative sensory attributes, and may result in substantial economic losses. The objective of this study was to use the electronic tongue (e-tongue) and flash sensory profiling (FP) to evaluate changes in red wine over time due to the presence of different spoilage microorganisms. Merlot wine was inoculated with one of the following microorganisms: Brettanomyces bruxellensis, Lactobacillus brevis, Pediococcus parvulus, or Acetobacter pasteurianus. These wines were analyzed weekly until Day 42 using the e-tongue and FP, with microbial plate counts. Over time, both FP and e-tongue differentiated the wines. The e-tongue showed a low discrimination among microorganisms up to Day 14 of storage. However, at Day 21 and continuing to Day 42, the e-tongue discriminated among the samples with a discrimination index of 91. From the sensory FP data, assessors discriminated among the wines starting at Day 28. Non-spoilage terms were used to describe the wines at significantly higher frequency for all time points until Day 42, at which point the use of spoilage terms was significantly higher (p < 0.05). These results suggest that application of these novel techniques may be the key to detecting and limiting financial losses associated with wine faults.

E-sensing and nanoscale-sensing devices associated with data processing algorithms applied to food quality control: a systematic review

Devices of human-based senses such as e-noses, e-tongues and e-eyes can be used to analyze different compounds in several food matrices. These sensors allow the detection of one or more compounds present in complex food samples, and the responses obtained can be used for several goals when different chemometric tools are applied. In this systematic review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, to address issues such as e-sensing with chemometric methods for food quality control (FQC). A total of 109 eligible articles were selected from PubMed, Scopus and Web of Science. Thus, we predicted that the association between e-sensing and chemometric tools is essential for FQC. Most studies have applied preliminary approaches like exploratory analysis, while the classification/regression methods have been less investigated. It is worth mentioning that non-linear methods based on artificial intelligence/machine learning, in most cases, had classification/regression performances superior to non-liner, although their applications were seen less often. Another approach that has generated promising results is the data fusion between e-sensing devices or in conjunction with other analytical techniques. Furthermore, some future trends in the application of miniaturized devices and nanoscale sensors are also discussed.

Optimizing the acceleration of Cheddar cheese ripening using response surface methodology by microbial protease without altering its quality features

Cheddar cheese proteolysis were accelerated employing Penicillium candidum PCA1/TT031 protease into cheese curd. In the present study, several of the significant factors such as protease purification factor (PF), protease concentration and ripening time were optimized via the response surface methodology (RSM). The ideal accelerated Cheddar cheese environment consisted of 3.12 PF, 0.01% (v/v) protease concentration and 0.6/3 months ripening time at 10 °C. The RSM models was verified to be the most proper methodology for the maintain of chosen Cheddar cheese. Under this experimental environment, the pH, acid degree value (ADV), moisture, water activity (a_w), soluble nitrogen (SN)%, fat and overall acceptability were found to be 5.4, 6.6, 35%, 0.9348, 18.8%, 34% and 13.6, respectively of ideal Cheddar cheese. Furthermore, the predicted and experimental results were in significant agreement, which confirmed the validity and reliability of the suggested method. In spite of the difference between the ideal and commercial Cheddar cheese in the concentration of some of amino acids and free fatty acids, the sensory evaluation did not show any significant difference in aroma profile between them.