Spectroscopic techniques for monitoring changes in the quality of milk and other dairy products during processing and storage

Determination of Digestible Indispensable Amino Acid Scores for Plant Proteins and Skim Milk Powder Measured in Pigs

The objective was to determine the digestible indispensable amino acid score (DIAAS) of food sources using the pig model and in vitro procedures. The standardized ileal digestibility (SID) of amino acids (AA) in white rice, wheat, pea protein concentrate (PPC), soy protein isolate (SPI), and skim milk powder (SMP) were determined in pigs. Twelve barrows with an initial body weight of 28.4 kg (standard deviation = 4.0) equipped with a T-cannula at the distal ileum were allotted to a replicated 6 × 5 incomplete Latin square design with six diets and five periods. Five experimental diets contained each ingredient as the only source of AA. Additionally, a nitrogen-free diet was prepared to measure the basal endogenous losses of AA for the calculation of the SID of AA. The in vitro ileal disappearance (IVID) of crude protein (CP) in the five food sources was also determined using the conventional flask procedure and the multi-sample simultaneous in vitro procedure employing the DaisyII incubator. The DIAAS values for white rice were 42, 50, and 60 for infants, children, and adults, respectively. For wheat, the DIAAS values were 46, 56, and 66, respectively. The DIAAS values for PPC were 47, 58, and 69 for infants, children, and adults, respectively. For SPI, the values were 61, 74, and 87, respectively. The DIAAS for SMP were 82, 112, and 131 for infants, children, and adults respectively. The IVID of CP in SMP was greater (p < 0.05) than that in other sources. The DIAAS calculated based on IVID of CP were similar to those determined based on the SID of AA in pigs. In conclusion, white rice, wheat, pea protein concentrate, and soy protein isolate were observed to have DIAAS values less than 100, indicating that these ingredients need to be combined with other protein sources to meet the indispensable AA requirements. Skim milk powder had a DIAAS greater than 100 for children and adults.

Towards on-line cheese monitoring: Exploration of semi-hard cheeses using NIR and 1H NMR spectroscopy

This study aims to investigate the potential of using advanced spectroscopies for cheese quality monitoring. For this purpose, six semi-hard cheeses manufactured using lactic acid bacteria (LAB) and/or propionic acid bacteria (PAB) were explored using near-infrared spectroscopy (NIRS) and Proton Nuclear Magnetic Resonance (1H NMR) spectroscopy. The spectral data were analyzed using principal component analysis for extraction of possible discriminative patterns in quality parameters. The results show that the green analytical, but primarily bulk-sensitive, NIRS method was able to discriminate the cheese varieties primarily due to differences in the first overtone CH stretching region between 1650 and 1720 nm, in particular by the lactate methylene absorption at 1674 nm. A total of 25 metabolites were identified in the 1H NMR spectra of the cheese extracts, several of which were associated with the LAB and PAB metabolic pathways. PAB-associated metabolites include propionate, acetate, and glutamate, while LAB-associated metabolites include lactate and acetoin among others.

Chemometrics-driven monitoring of cheese ripening: a multimodal spectroscopic and scanning electron microscopy investigation

The integration of spectroscopic techniques with chemometrics offers a means to monitor quality changes in dairy products throughout processing and storage. This study employed Attenuated Total Reflectance-Mid-Infrared Spectroscopy (ATR-MIR) coupled with Independent Components Analysis (ICA), and 3D Front-Face Fluorescence Spectroscopy (FFFS) paired with Common Components and Specific Weight Analysis (CCSWA). The research focused on Cheddar cheeses aged for 1, 2, 3, and 5 years, alongside Comté cheeses aged for 6, 9, and 12 months. The adopted approach offered valuable insights into the intricate cheese aging process within the food matrix. The ICA proportions and CCSWA scores highlighted the significant impact of biochemical transformations during maturation on the aging process. The extracted independent components (ICs) revealed variations in the vibration modes of amides, lipids, amino acids, and organic acids, facilitating the distinction between different cheese age categories. Additionally, CCSWA outcomes identified age-related differences through shifts in tryptophan fluorescence characteristics as the cheeses aged. These results were consistent with the observed alterations in the microstructure of cheese samples over time, corroborated by Scanning Electron Microscopy (SEM) imagery. The introduced multimodal methodology serves as a significant asset for determining the ripening stage of various types of cheese, offering a detailed perspective of cheese maturation beneficial to the dairy industry and researchers.

Intelligent System/Equipment for Quality Deterioration Detection of Fresh Food: Recent Advances and Application

The quality of fresh foods tends to deteriorate rapidly during harvesting, storage, and transportation. Intelligent detection equipment is designed to monitor and ensure product quality in the supply chain, measure appropriate food quality parameters in real time, and thus minimize quality degradation and potential financial losses. Through various available tracking devices, consumers can obtain actionable information about fresh food products. This paper reviews the recent progress in intelligent detection equipment for sensing the quality deterioration of fresh foods, including computer vision equipment, electronic nose, smart colorimetric films, hyperspectral imaging (HSI), near-infrared spectroscopy (NIR), nuclear magnetic resonance (NMR), ultrasonic non-destructive testing, and intelligent tracing equipment. These devices offer the advantages of high speed, non-destructive operation, precision, and high sensitivity.

Emerging Technologies for Improving Properties, Shelf Life, and Analysis of Dairy Products

Processing results in several kinds of dairy products with variable properties and shelf lives that preserve and often enhance the unique nutritional and biological value of milk [...].

Fast concurrent determination of guaifenesin and pholcodine in formulations and spiked plasma using first derivative synchronous spectrofluorimetric approach

Guaifenesin and pholcodine are frequently co-formulated in certain dosage forms. A new fast first derivative synchronous spectrofluorometric method has been used for their simultaneous analysis in mixtures. Here, first derivative synchronous spectrofluorometry enabled the successful simultaneous estimation of guaifenesin at 283 nm and pholcodine at 275 nm using a wavelength difference (Δλ) of 40 nm. The method was fully validated following International Council of Harmonization guidelines. For guaifenesin and pholcodine, linearity was determined within the corresponding ranges of 0.05-0.30 and 0.10-6.0 μg/ml. The two drugs were effectively analyzed using the developed approach in their respective formulations, and the results showed good agreement with those attained using reference methods. The method demonstrated excellent sensitivity, with detection limits down to 0.007 and 0.030 μg/ml and quantitation limits of 0.020 and 0.010 μg/ml for guaifenesin and pholcodine, respectively. Therefore, the procedure was successful in determining these drugs simultaneously in vitro in spiked plasma samples and syrup dosage form. The developed methodology also offered an environmentally friendly advantage by utilizing water as the optimal diluting solvent throughout the whole work. Different greenness approaches were investigated to ensure the method's ecofriendly properties.

IoT-Enabled Electronic Nose System for Beef Quality Monitoring and Spoilage Detection

Food spoilage is a major concern in the food industry, especially for highly perishable foods such as beef. In this paper, we present a versatile Internet of Things (IoT)-enabled electronic nose system to monitor food quality by evaluating the concentrations of volatile organic compounds (VOCs). The IoT system consists mainly of an electronic nose, temperature/humidity sensors, and an ESP32-S3 microcontroller to send the sensors' data to the server. The electronic nose consists of a carbon dioxide gas sensor, an ammonia gas sensor, and an ethylene gas sensor. This paper's primary focus is to use the system for identifying beef spoilage. Hence, the system performance was examined on four beef samples stored at different temperatures: two at 4 °C and two at 21 °C. Microbial population quantifications of aerobic bacteria, Lactic Acid Bacteria (LAB), and Pseudomonas spp., in addition to pH measurements, were conducted to evaluate the beef quality during a period of 7 days to identify the VOCs concentrations that are associated with raw beef spoilage. The spoilage concentrations that were identified using the carbon dioxide, ammonia, and ethylene sensors were 552 ppm-4751 ppm, 6 ppm-8 ppm, and 18.4 ppm-21.1 ppm, respectively, as determined using a 500 mL gas sensing chamber. Statistical analysis was conducted to correlate the bacterial growth with the VOCs production, where it was found that aerobic bacteria and Pseudomonas spp. are responsible for most of the VOCs production in raw beef.

Incorporation of gelatin and Fe2+ increases the pH-sensitivity of zein-anthocyanin complex films used for milk spoilage detection

In this study, blueberry anthocyanins, gelatin and Fe²⁺ were incorporated into zein matrix via electrospinning method to prepare colorimetric indicator films for monitoring milk freshness. Gelatin and Fe²⁺ were incorporated into the film to improve visual discrimination of indicator films' color changes in milk with different freshness degrees and in solution with pH 3-7. Results of SEM, FT-IR and XRD showed that there were intermolecular hydrogen bonds among components, which associated with the larger color difference of indicator films. UV-vis spectral analysis showed that blueberry anthocyanin solutions containing both gelatin and Fe²⁺ displayed the highest intensity absorption peaks. The optimal ability to distinguish the pH (3-7) of solutions was presented by the indicator film incorporating gelatin (1% (w/v)) and Fe²⁺ (0.07 mg/mL). Gelatin and Fe²⁺ increased the color-responsive sensitivity of the indicator film to pH. The film could be successfully used to detect the freshness of milk, whose color changes were visually perceivable: from purple black (fresh milk) to royal purple (spoiling milk) and then to violet red (spoiled milk). The color parameters (L*, a*, R, G and B) of the film revealed a high correlation with the pH/acidity of the milk during storage. The successful application of the indicator film embedding gelatin and Fe²⁺ for monitoring milk quality changes indicated that the addition of special substances could provide great potential for monitoring freshness and preparing intelligent packaging of food.

Evaluating the Feasibility of a Low-Field Nuclear Magnetic Resonance (NMR) Sensor for Manure Nutrient Prediction

Livestock manure is typically applied to fertilize crops, however the accurate determination of manure nutrient composition through a reliable method is important to optimize manure application rates that maximize crop yields and prevent environmental contamination. Existing laboratory methods can be time consuming, expensive, and generally the results are not provided prior to manure application. In this study, the evaluation of a low-field nuclear magnetic resonance (NMR) sensor designated for manure nutrient prediction was assessed. Twenty dairy manure samples were analyzed for total solid (TS), total nitrogen (TN), ammoniacal nitrogen (NH4-N), and total phosphorus (TP) in a certified laboratory and in parallel using the NMR analyzer. The linear regression of NMR prediction versus lab measurements for TS had an R2 value of 0.86 for samples with TS < 8%, and values of 0.94 and 0.98 for TN and NH4-N, respectively, indicating good correlations between NMR prediction and lab measurements. The TP prediction of NMR for all samples agreed with the lab analysis with R2 greater than 0.87. The intra- and inter-sample variations of TP measured by NMR were significantly larger than other parameters suggesting less robustness in TP prediction. The results of this study indicate low-field NMR is a rapid method that has a potential to be utilized as an alternative to laboratory analysis of manure nutrients, however, further investigation is needed before wide application for on farm analysis.