Detection of Lipid Oxidation in Infant Formulas: Application of Infrared Spectroscopy to Complex Food Systems

Crystal size, a key character of lactose crystallization affecting microstructure, surface chemistry and reconstitution of milk powder

Lactose crystallization during storage deteriorates reconstitution performance of milk powders, but the relationship between lactose crystallization and reconstitution is inexplicit. The objective of this study is to characterize crystalline lactose in the context of formulation and elucidate the complex relationship between lactose crystallization and powder functionality. Lactose in Skim Milk Powder (SMP), Whole Milk Powder (WMP) and Fat-Filled Milk Powder (FFMP) stored under 23 %, 53 % and 75 % Relative Humidity (RH) at 25  ℃ for four months was compared. Lactose, surface chemistry and microstructure of FFMP stored at 25 ℃ and 40 ℃ at 23 % to 75 % RH for four months were also analyzed and interpreted. At the same RH, FFMP crystallized in the same pattern as WMP. At 53 % RH, FFMP and WMP differentiated from SMP in terms of lactose morphology as well as the ratio between anhydrous α-lactose and anhydrous β-lactose. Lactose remained amorphous at 23 % RH, crystallized predominantly to α/β-lactose (1:4) at 40 to 58 % RH and to α-lactose monohydrate at 75 % RH. The crystallinity index was similar for all powders containing crystalline lactose. The estimated crystallite size increased from approx. 0.1 to 20 µm with increasing RH and temperature. When amorphous lactose crystallized into crystals below approx. 0.1 µm at 25 °C and 43 % RH, the microstructure and surface lipid were comparable to that of the reference powder. This powder reconstituted into a stable suspension system comparable to that of reference (well performing) powders. These results demonstrate that crystallite size is the key property linking lactose crystallization and reconstitution. Our finding thus indicates limiting crystallite size is important for maintaining desired product quality.

Rapid detection of micronutrient components in infant formula milk powder using near-infrared spectroscopy

In order to achieve rapid detection of galactooligosaccharides (GOS), fructooligosaccharides (FOS), calcium (Ca), and vitamin C (Vc), four micronutrient components in infant formula milk powder, this study employed four methods, namely Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), Normalization (Nor), and Savitzky-Golay Smoothing (SG), to preprocess the acquired original spectra of the milk powder. Then, the Competitive Adaptive Reweighted Sampling (CARS) algorithm and Random Frog (RF) algorithm were used to extract representative characteristic wavelengths. Furthermore, Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) models were established to predict the contents of GOS, FOS, Ca, and Vc in infant formula milk powder. The results indicated that after SNV preprocessing, the original spectra of GOS and FOS could effectively extract feature wavelengths using the CARS algorithm, leading to favorable predictive results through the CARS-SVR model. Similarly, after MSC preprocessing, the original spectra of Ca and Vc could efficiently extract feature wavelengths using the CARS algorithm, resulting in optimal predictive outcomes via the CARS-SVR model. This study provides insights for the realization of online nutritional component detection and optimization control in the production process of infant formula.

Development of rapid and non-destructive electric nose (E-nose) system for shelf life evaluation of different edible seeds

Shelf life estimation is crucial in ensuring the quality of food products. However, traditional methods are time-consuming and inefficient. Therefore, there is an urgent need for simple, efficient and rapid techniques for quality assessments. An electronic nose (E-nose) serves as a solution by rapidly and accurately detecting release of volatile organic compounds (VOCs) during food deterioration. This study aims to develop Arduino-Uno R3 microprocessor based E-nose, equipped with MQ4, MQ5, MQ9 and MQ135 sensors for evaluating shelf life of different edible seeds over the storage period of 150 days. Sensor values were recorded, revealing a significant increase (p-value ≤ 0.05) in MQ5 sensor readings for Nigella seeds from 349 to 480. Sensor values were positively correlated with physical, microbiological and Fourier-transform infrared (FTIR) spectroscopy parameters. Maximum peak shifts were observed from 3000 cm^-1 to 2800 cm^-1 and 1500 cm^-1 to 1000 cm^-1 wavenumbers. Hence, this study provides successful E-nose system to determine shelf life of seeds.

Rapid Identification of Insecticide- and Herbicide-Tolerant Genetically Modified Maize Using Mid-Infrared Spectroscopy

Genetically modified (GM) technology is of great significance for increasing crop production, protecting biodiversity, and reducing environmental pollution. However, with the frequent occurrence of safety events regarding GM foods, more and more disputes have arisen over the potential safety of transgenic technology. It is particularly necessary to find a fast and accurate method for transgenic product identification. In this research, mid-infrared spectroscopy, coupled with chemometric methods, was applied to discriminate GM maize from its non-GM parent. A total of 120 GM maize and 120 non-GM maize samples were prepared, and the spectral information in the range of 400–4000 cm−1 was collected. After acquiring the spectra, wavelet transform (WT) was used to preprocess the data, and k-means was carried out to split all samples into calibration and prediction sets in the ratio of 2:1. Principal component analysis (PCA) was then conducted to qualitatively distinguish the two types of samples, and an apparent cluster was observed. Since the full spectrum covered a large amount of data and redundant information, we adopted the successive projections algorithm (SPA) to select optimal wavelengths for further analysis. Chemometrics, including partial least squares-discriminant analysis (PLS-DA), the k-nearest neighbor algorithm (KNN), and the extreme learning machine (ELM), were performed to establish classification models based on full spectra and optimal wavelengths. The overall results indicated that ELM models based on full spectra and optimal spectra showed better accuracy and reliability, with a 100% recognition rate in the calibration set and a 98.75% recognition rate in the prediction set. It has been confirmed that mid-infrared spectroscopy, combined with chemometric methods, can be a novel approach to identify transgenic maize.

Research Progress of Applying Infrared Spectroscopy Technology for Detection of Toxic and Harmful Substances in Food

In recent years, food safety incidents have been frequently reported. Food or raw materials themselves contain substances that may endanger human health and are called toxic and harmful substances in food, which can be divided into endogenous, exogenous toxic, and harmful substances and biological toxins. Therefore, realizing the rapid, efficient, and nondestructive testing of toxic and harmful substances in food is of great significance to ensure food safety and improve the ability of food safety supervision. Among the nondestructive detection methods, infrared spectroscopy technology has become a powerful solution for detecting toxic and harmful substances in food with its high efficiency, speed, easy operation, and low costs, while requiring less sample size and is nondestructive, and has been widely used in many fields. In this review, the concept and principle of IR spectroscopy in food are briefly introduced, including NIR and FTIR. Then, the main progress and contribution of IR spectroscopy are summarized, including the model’s establishment, technical application, and spectral optimization in grain, fruits, vegetables, and beverages. Moreover, the limitations and development prospects of detection are discussed. It is anticipated that infrared spectroscopy technology, in combination with other advanced technologies, will be widely used in the whole food safety field.

Effects of nisin and EDTA on morphology and properties of thermoplastic starch and PBAT biodegradable films for meat packaging

Biodegradable active packaging was produced by compounding nisin (3, 6 and 9%) and nisin-ethylenediaminetetraacetic acid (EDTA) (3 and 6%) mixtures with poly(butylene adipate terephthalate) and thermoplastic starch blends (PBAT/TPS) by blown-film extrusion. Nisin and EDTA interacted with polymers, involving CO stretching of ester bonds and increased compatibility. This plasticized the films and modified the crystallinity, surface roughness and thermal relaxation behavior. Barrier properties were improved due to modified hydrophilic-hydrophobic properties, compact structures and crystallites that restricted vapor and oxygen permeation. PBAT/TPS films containing EDTA and nisin effectively inhibited lipid degradation in pork tissues corresponding with stabilizing the CO ester bond of triacylglycerol. Microbial growth was also inhibited, particularly in EDTA-containing films up to 1.4 log. Inactivation of microorganisms stabilized redness and delayed meat discoloration, preserving the quality of packaged pork. Interaction between nisin, EDTA and polymers modified the morphology and film properties and functionalized biodegradable food packaging to inactivate microorganisms.

Nondestructive Analysis of Internal Quality in Pears with a Self-Made Near-Infrared Spectrum Detector Combined with Multivariate Data Processing

The consumption of pears has increased, thanks not only to their delicious and juicy flavor, but also their rich nutritional value. Traditional methods of detecting internal qualities (e.g., soluble solid content (SSC), titratable acidity (TA), and taste index (TI)) of pears are reliable, but they are destructive, time-consuming, and polluting. It is necessary to detect internal qualities of pears rapidly and nondestructively by using near-infrared (NIR) spectroscopy. In this study, we used a self-made NIR spectrum detector with an improved variable selection algorithm, named the variable stability and cluster analysis algorithm (VSCAA), to establish a partial least squares regression (PLSR) model to detect SSC content in snow pears. VSCAA is a variable selection method based on the combination of variable stability and cluster analysis to select the infrared spectrum variables. To reflect the advantages of VSCAA, we compared the classical variable selection methods (synergy interval partial least squares (SiPLS), genetic algorithm (GA), successive projections algorithm (SPA), and bootstrapping soft shrinkage (BOSS)) to extract useful wavelengths. The PLSR model, based on the useful variables selected by SiPLS-VSCAA, was optimal for measuring SSC in pears, and the correlation coefficient of calibration (Rc), root mean square error of cross validation (RMSECV), correlation coefficient of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) were 0.942, 0.198%, 0.936, 0.222%, and 2.857, respectively. Then, we applied these variable selection methods to select the characteristic wavelengths for measuring the TA content and TI value in snow pears. The prediction PLSR models, based on the variables selected by GA-BOSS to measure TA and that by GA-VSCAA to detect TI, were the best models, and the Rc, RMSECV, Rp and RPD were 0.931, 0.124%, 0.912, 0.151%, and 2.434 and 0.968, 0.080%, 0.968, 0.089%, and 3.775, respectively. The results showed that the self-made NIR-spectrum detector based on a portable NIR spectrometer with multivariate data processing was a good tool for rapid and nondestructive analysis of internal quality in pears.