Raman spectroscopy to assess water holding capacity in muscle from fast and slow growing broilers

Rapid and non-destructive quality methods that can rapidly scan large product numbers in early stages of production are attractive for meat production. Chicken breast muscles from low- and high-growth rate (GR) broilers were segregated into extremes of broilers with weight gain of ≤250 g/wk and ≥299 g/wk, respectively. Breast muscle was also segregated into extremes of low- and high-water holding capacity (WHC) of ≤4.08 g/100 g or ≥ 89.26 g/100 g muscle, respectively. Raman spectroscopy was used to evaluate differences in spectra of the extremes of broilers that differed in GR or muscle that differed in WHC. Spectra was collected at 785 nm excitation, 500 mW laser power, 10 s total integration time. There were 7 significantly different bands (P < 0.05) from growth rate population spectrum: 510, 1270, 1354, 1535, 1652, 1737, 1863 cm⁻¹; and 4 significantly different bands (P < 0.05) from WHC population spectrum: 538, 691, 1367, 1743 cm⁻¹. Differences in the trptophan environment, cysteine and amide I and III structure influenced WHC. The results can potentially be used to rapidly identify muscle attribute variations for customized handling in further processing.     

Fast detection of cyclopiazonic acid in cheese using Fourier transform mid-infrared ATR spectroscopy

A simple and rapid Fourier transform mid-infrared spectroscopic method with attenuated total reflection was developed for the detection of cyclopiazonic acid in ewe's cheese samples. A partial least-squares calibration model for the prediction of cyclopiazonic acid content in ewe medium-ripened cheese, based on spectra data, revealed to be the best approach. The PLS model was tested by cross-validation in order to minimize standard error of the model and the relevant RMSECV (root of mean-squared error in cross-validation) was calculated to be 0.05 μg/g. In parallel experiments, cyclopiazonic acid content was estimated using the developed SPME-HPLC/UV method and different cheese samples were analysed using both techniques. The method appears to be useful for a qualitative and semi-quantitative screening of a large number of cheese samples, important in food contaminants monitoring. Since no sample pretreatment is required at all, the analysis times are dramatically shortened in comparison with any chromatographic procedure.     

Prediction of sugars and acids in Chinese rice wine by mid-infrared spectroscopy

The use of Fourier transform mid-infrared spectroscopy (FT-MIR) for the rapid determination of sugars and acids in Chinese rice wine was presented in this study. Calibration models were developed by partial least squares regression (PLSR) for eleven parameters related to sugar content and acidity—namely, total sugar, non-sugar solid, glucose, maltose, isomaltotriose, isomaltose, panose, total acid, amino acid nitrogen, pH and lactic acid. In the calibration step, most of the parameters were accurately determined, obtaining regression coefficients of calibration (r_cal) ranging from 0.821 to 0.991. In validation, regression coefficients of validation (r_val) obtained for most parameters were higher than 0.85. Unsatisfactory predictions were obtained for isomaltotriose and isomaltose with r_val being 0.488 and 0.716, respectively. The residual predictive deviation (RPD) values were also higher than or close to 2.0 for all the parameters except for isomaltose and isomaltotriose. Overall, the results indicate that MIR spectroscopy can be applied to the quality determination of Chinese rice wine.     

Elucidation of structural changes in soy protein isolate upon heating by Raman spectroscopy

Changes in protein secondary and tertiary structure of soy protein isolate (SPI) upon heating process were investigated by Raman spectroscopy. SPI refrigerated and heated at 70, 100 and 200 °C was analysed. Non-significant differences in secondary structure (α-helix, β-sheet, unordered and turn) were observed in SPI upon heating at these temperatures. However, comparison of the Raman spectra of SPI refrigerated and heated at 70, 100 and 200 °C reveals changes in hydrophobic group environments. An intensity decreasing trend of the 1340 cm⁻¹ band attributable to tryptophan vibrations was observed, which involves an increase in solvent exposure of tryptophan residues. The spectral results also showed intensity increasing trends for the 1450 and 2935 cm⁻¹ bands of SPI upon heating, which can be ascribed to breaking of hydrophobic contacts and subsequent solvent exposure of the corresponding hydrophobic aliphatic groups. Finally, it was found tertiary structural transitions towards greater proportions of buried tyrosine residues upon heating of SPI.     

Evaluation of structural changes in raw and heated meat batters prepared with different lipids using Raman spectroscopy

Structural changes, textural properties, water- and fat-binding properties and their relationships in meat batters prepared with different lipids and with heating were studied by Raman spectroscopy. Results revealed that the meat batters prepared with soybean oil (SO) showed the lowest fluid losses, greatest (P < 0.05) hardness, springiness, cohesiveness, chewiness, resilience values compared with batters prepared with pork fat (PF) or butter (DB). There was a significant decrease (P < 0.05) in α-helix content accompanied by a significant increase (P < 0.05) in β-sheet structure after heating in PF and SO samples, but no significant (P > 0.05) difference was found in DB batters. A positive significant (P < 0.05) correlation between β-sheet structure and textural properties was found in heated samples. Further, there was a significant positive (P < 0.05) correlation between α-helical structure and total expressible fluid and a significant negative (P < 0.05) correlation between β-sheet structure and total expressible fluid. Results suggest that different lipid additions and thermal treatments induced different changes in meat proteins structural, expressible fluid, and textural properties of meat batters.     

Structural changes of myoglobin in pressure-treated pork meat probed by resonance Raman spectroscopy

Pork meat was pressurised at 600–700 MPa under conditions applicable for non-thermal food preservation and studied by resonance Raman spectroscopy with 413-nm excitation to probe selectively myoglobin, which is the origin of the red colour of meat. The spectra of intact, non-pressurised meat tissue exclusively display the resonance Raman bands of the ferrous deoxy-form of myoglobin whereas upon pressure treatment a new six-coordinated low spin ferrous species is formed (>60%), that is assigned to a bis–histidine complex including the distal histidine 64. This structural change is associated with a shift of the electronic transitions of the haeme and thus affects the colour of the meat. In contrast, solutions containing myoglobin extracted from pressurised and non-pressurised pork meat give rise to resonance Raman spectra characteristic of the ferrous oxy-form of myoglobin, evidently due to the accessibility of the proteins for oxygen in solution. Upon pressure treatment of the extracted myoglobin solution, the oxy-form is partially converted to the met-(like) ferric form implying a pressure-induced oxidation of the haeme. Thus, this structural transition does not only cause a colour change but also may initiate unwanted oxidative side reactions involving further components of meat. Evidently, such effects can be largely avoided when the oxy- to deoxy-myoglobin ratio is kept small prior to pressure treatment.     

Rapid and simple quantitative identification of Listeria monocytogenes in cheese by isothermal sequence exchange amplification based on surface-enhanced Raman spectroscopy

Foodborne pathogens detection is important to ensure food safety and human health. In this study, we designed a comet structure to rapidly and sensitively detect foodborne Listeria monocytogenes. This method combined isothermal sequence exchange amplification (SEA) and surface-enhanced Raman spectroscopy. Listeria monocytogenes DNA could be rapidly amplified at a constant temperature via SEA with a pair of modified primers, which rendered the precise thermal control instrumentation unnecessary. Efficient SEA amplification generated a large number of DNA duplexes that could be easily captured by streptavidin-modified magnetic bead and Au^MB@Ag-isothiocyanate fluorescein antibody (anti-FITC). Au^MB@Ag-anti-FITC was used as a signal probe, which generated a significant excitation signal at 1,616 cm^?1 for quantitative detection and analysis. The results displayed sensitive detection of L. monocytogenes in cheese from 2.0 × 10¹ cfu/mL to 2.0 × 10⁶ cfu/mL within 1.0 h with a detection limit of 7.8 cfu/mL. Furthermore, this comet structure displayed the desirable specificity as its specific primers and amplified DNA ends were attached to streptavidin-modified magnetic beads and Au^MB@Ag-anti-FITC, respectively. We expected that the method devised would provide a promising new approach to screening for L. monocytogenes and guarantee the microbiological safety of dairy products.     

拉曼光谱在粮食检测中的应用

拉曼光谱技术是一种能够实现快速、无损检测的高效检测技术,且加上拉曼光谱仪的操作简单、结果准确等特点,目前该技术已被广泛的应用于生物、医药、材料等诸多领域,在粮食品质、结构检测方面同样具有良好的应用前景。数据显示,每年受重金属污染的粮食高达1200万吨,引起了人们的担忧及重视,如何实现粮食污染的简单快速鉴别,成为人们关心的问题。在此背景下,本文就当前拉曼光谱这一无损检测技术在粮食检测领域中的应用进行了综述,以期为今后粮食的快速检测方法提供相应支持。文章介绍了拉曼光谱技术的原理、分类以及拉曼光谱仪的结构,综述了拉曼光谱技术在大米、小麦、玉米等主要粮食检测中的应用现状,并对其今后的研究发展前景进行展望。     

Prediction of processed cheese instrumental texture and meltability by mid-infrared spectroscopy coupled with chemometric tools

The objective of this study was to determine the potential of mid-infrared spectroscopy coupled with multidimensional statistical analysis for the prediction of processed cheese instrumental texture and meltability attributes. Processed cheeses (n = 32) of varying composition were manufactured in a pilot plant. Following two and four weeks storage at 4 °C samples were analysed using texture profile analysis, two meltability tests (computer vision, Olson and Price) and mid-infrared spectroscopy (4000–640 cm⁻¹). Partial least squares regression was used to develop predictive models for all measured attributes. Five attributes were successfully modelled with varying degrees of accuracy. The computer vision meltability model allowed for discrimination between high and low melt values (R² = 0.64). The hardness and springiness models gave approximate quantitative results (R² = 0.77) and the cohesiveness (R² = 0.81) and Olson and Price meltability (R² = 0.88) models gave good prediction results.     

Evaluation of chemical parameters in soft mold-ripened cheese during ripening by mid-infrared spectroscopy

The suitability of mid-infrared spectroscopy (MIR) to follow the evolution throughout ripening of specific physicochemical parameters in Camembert-type cheeses was evaluated. The infrared spectra were obtained directly from raw cheese samples deposited on an attenuated total reflectance crystal. Significant correlations were observed between physicochemical data, pH, acid-soluble nitrogen, nonprotein nitrogen, ammonia (NH₄⁺), lactose, and lactic acid. Dry matter showed significant correlation only with lactose and nonprotein nitrogen. Principal components analysis factorial maps of physicochemical data showed a ripening evolution in 2 steps, from d 1 to d 7 and from d 8 to d 27, similar to that observed previously from infrared spectral data. Partial least squares regressions made it possible to obtain good prediction models for dry matter, acid-soluble nitrogen, nonprotein nitrogen, lactose, lactic acid, and NH₄⁺ values from spectral data of raw cheese. The values of 3 statistical parameters (coefficient of determination, root mean square error of cross validation, and ratio prediction deviation) are satisfactory. Less precise models were obtained for pH.     

Determination of fat,protein, moisture,and salt content of Cheddar cheese using mid-infrared transmittance spectroscopy

The objective of our work was to develop and evaluate the performance of a rapid method for measuring fat, protein, moisture, and salt content of Cheddar cheese using a combination mid-infrared (MIR) transmittance analysis and an in-line conductivity sensor in an MIR milk analyzer. Cheddar cheese was blended with a dissolving solution containing pentasodium triphosphate and disodium metasilicate to achieve a uniform, particle-free dispersion of cheese, which had a fat and protein content similar to milk and could be analyzed using a MIR transmittance milk analyzer. Annatto-colored Cheddar cheese samples (34) from one cheese factory were analyzed using reference chemistry methods for fat (Mojonnier ether extraction), crude protein (Kjeldahl), moisture (oven-drying total solids), and salt (Volhard silver nitrate titration). The same 34 cheese samples were also dissolved using the cheese dissolver solution, and then run through the MIR and used for calibration. The reference testing for fat and crude protein was done on the cheese after dispersion in the dissolver solution. Validation was done using a total of 36 annatto-colored Cheddar cheese samples from 4 cheese factories. The 36 validation cheese samples were also analyzed using near-infrared spectroscopy for fat, moisture, and the coulometric method for salt in each factory where they were produced. The validation cheeses were also tested using the same chemical reference methods that were used for analysis of the calibration samples. Standard error of prediction (SEP) values for moisture and fat on the near-infrared spectroscopy were 0.30 and 0.45, respectively, whereas the MIR produced SEP values of 0.28 and 0.23 for moisture (mean 36.82%) and fat (mean 34.0%), respectively. The MIR also out-performed the coulometric method for salt determination with SEP values of 0.036 and 0.139 at a mean level of salt of 1.8%, respectively. The MIR had an SEP value of 0.19 for estimation at a mean level of 24.0% crude protein, which suggests that MIR could be an easy and effective way for cheese producers to measure protein to determine protein recovery in cheese making.     

X-ray computerized microtomography and confocal Raman microscopy as complementary techniques to conventional imaging tools for the microstructural characterization of Cheddar cheese

This study explored the use of X-ray computerized microtomography (micro-CT) and confocal Raman microscopy to provide complementary information to well-established techniques, such as confocal laser scanning microscopy (CLSM), for the microstructural characterization of cheese. To evaluate the potential of these techniques, 5 commercial Cheddar cheese samples, 3 with different ripening times and 2 with different fat contents, were analyzed. Confocal laser scanning microscopy was particularly useful to describe differences in fat and protein distribution, especially between the 2 samples with different fat contents. The quantitative data obtained through image analysis correlated well with the nutritional information provided in the product labels. Conversely, micro-CT was more advantageous for studying the size and spatial distribution of microcrystals present within the cheese matrix. Two types of microcrystals were identified that differed in size, shape, and X-ray attenuation. The smallest, with a diameter of approximately 10 to 20 μm, were more abundant in the samples and presented a more uniform roundish shape and higher X-ray attenuation. Larger and more heterogeneous crystals with diameters reaching 50 μm were also observed in scarcer numbers and showed lower X-ray attenuation. Confocal Raman microscopy was useful not only for identifying the distribution of all these components but also allowed comparing the presence of micronutrients such as carotenoids in the cheeses and provided compositional information on the crystals detected. Small and large crystals were identified as calcium phosphate and calcium lactate, respectively. Overall, using micro-CT, confocal Raman microscopy, and CLSM in combination generated novel and complementary information for the microstructural and nutritional characterization of Cheddar cheese. These techniques can be used to provide valuable knowledge when studying the effect of milk composition, processing, and maturation on the cheese quality attributes.     

Protein and water structural changes in fish surimi during gelation as revealed by isotopic H/D exchange and Raman spectroscopy

Structural changes of proteins and water during gelation of fish surimi, have been studied by isotopic H/D exchange of water and Raman spectroscopy assisted by monitoring of rheological characteristics, in order to get insights into the structural and functional properties of surimi gels. The results indicate the following: (i) Protein hydrogen bond rearrangements occur involving mainly α-helix to β-sheet transition, (ii) the relative intensity of the symmetric H₂O stretching band near 3220 cm⁻¹ tends to decrease upon gelation, (iii) H/D exchange reveal a slower deuteration kinetics in the gels as compared to the surimi, (iv) the low temperature scanning electron microscopy shows a smaller pore size of the gel network as compared to the surimi, and suggests that water domains in gel are more inaccessible to D₂O, which is consistent with higher water holding capacity in the gel.     

利用拉曼光谱技术识别溴氰菊酯和 啶虫脒农药的研究

目的 初步建立溴氰菊酯农药和啶虫脒农药的拉曼光谱研究方法。方法 利用激光拉曼光谱技术, 将农药溶液装满铝盒, 采集农药溶液的拉曼光谱曲线, 运用自主编写的程序对光谱曲线进行预处理, 以去除光谱噪音并扣除荧光背景, 进而探讨溴氰菊酯农药和啶虫脒农药的快速无损检测方法。结果 溴氰菊酯农药和啶虫脒农药的拉曼特征频率较为丰富, 选取574 cm?1、736 cm?1处的拉曼信号可识别溴氰菊酯农药, 检测限为2500 mg/kg。选取752 cm?1、840 cm?1处的拉曼信号可识别啶虫脒农药, 当溶剂的丙酮-水比例为1:5时, 最适合作为检测溶剂配置啶虫脒农药溶液, 为下一步确定啶虫脒检测限奠定基础。结论 本研究对实现拉曼光谱技术检测果蔬表面溴氰菊酯农药和啶虫脒农药具有重要意义。     

Cheddar cheese classification based on flavor quality using a novel extraction method and Fourier transform infrared spectroscopy

Analysis of Cheddar cheese flavor using trained sensory and grading panels is expensive and time consuming. A rapid and simple solvent extraction procedure in combination with Fourier transform infrared spectroscopy was developed for classifying Cheddar cheese based on flavor quality. Fifteen Cheddar cheese samples from 2 commercial production plants were ground into powders using liquid nitrogen. The water-soluble compounds from the cheese powder, without interfering compounds such as fat and protein, were extracted using water, chloroform, and ethanol. Aliquots (10 μL) of the extract were placed on a zinc selenide crystal, vacuum dried, and scanned in the mid-infrared region (4,000 to 700 cm⁻¹). The infrared spectra were analyzed by soft independent modeling of class analogy (SIMCA) for pattern recognition. Sensory flavor quality of these cheeses was determined by trained quality assurance personnel in the production facilities. The SIMCA models provided 3-dimensional classification plots in which all the 15 cheese samples formed well-separated clusters. The orientation of the clusters in 3-dimensional space correlated well with their cheese flavor characteristics (fermented, unclean, low flavor, sour, good Cheddar, and so on). The discrimination of the samples in the SIMCA plot was mainly due to organic acids, fatty acids and their esters, and amino acids (1,450 to 1,350 and 1,200 to 990 cm⁻¹), which are known to contribute significantly to cheese flavor. The total analysis time, including the sample preparation time, was less than 20 min per sample. This technique can be a rapid, inexpensive, and simple tool to the cheese industry for predicting the flavor quality of cheese.     

表面增强拉曼光谱技术在食品安全检测领域中的应用

表面增强拉曼光谱(SERS)分析技术是基于拉曼散射现象,通过将被测物质吸附在SERS衬底表面增强其“指纹”信息,以实现痕量检测,具有前处理简单、灵敏度高、操作简单、可实现现场实时无损检测等优点,在食品安全快速检测领域备受关注。文章介绍了各类SERS衬底的特点和局限性,总结了近年来SERS分析技术在食品安全检测领域的研究进展,讨论了其商业应用所面临的挑战,并对其未来发展进行了展望。