Prediction of white button mushroom (Agaricus bisporus) moisture content using hyperspectral imaging

Hyperspectral imaging is a non-contact, non-destructive technique that combines spectroscopy and imaging to extract information from a sample. This technology has recently emerged as a powerful technique for food analysis. In this study, the potential of hyperspectral imaging (HSI) to predict white button mushroom moisture content (MC) was investigated. Mushrooms were subjected to dehydration at 45 ± 1 °C for different time periods (0, 30, 60 and 120 min) to obtain representative samples at different moisture levels (93.40 ± 0.62%, 82.76 ± 2.11%, 73.20 ± 2.60% and 60.89 ± 4.32% wet basis [wb]). Hyperspectral images of the mushrooms were obtained using a pushbroom system operating in the wavelength range of 400–1000 nm. Hunter L, a and b colour values of the mushrooms were also measured. The average reflectance spectra of samples at different MC levels were obtained and Partial Least Square Regression (PLSR) models were built to predict mushroom moisture content. To reduce the spectral variability caused by factors unrelated to MC such as scattering effects and differences in sample height, different spectral pre-treatments were applied. The Standard Normal Variate (SNV) transformation was found to be the best approach among the wavelength range studied, resulting in the greatest reduction in Root Mean Square Error of Cross Validation (RMSECV) and Root Mean Square Error of Prediction (RMSEP) for a 4-component PLSR model. RMSECV of 5.50 (% wb) and RMSEP of 5.58 (% wb) were obtained for the calibration and test sets of data, respectively. Prediction maps were generated from hyperspectral data to show the predictive model performance at pixel level. This study shows the potential of hyperspectral imaging for prediction of mushroom moisture content in the studied wavelength range. The implemented method highlighted contrast between areas of different moisture content to achieve better knowledge of dehydration distribution over the mushroom surface.     

Nondestructive determination of pear internal quality indices by visible and near-infrared spectrometry

The relationships between the nondestructive visible and near-infrared (Vis-NIR) measurements and the internal quality indices of pear fruit were established, and the potential of Vis-NIR spectrometry technique was investigated for its ability to nondestructively measure soluble solids content (SSC) and firmness of intact pear fruit. Intact pear fruit were measured by diffuse reflectance Vis-NIR in 350–1800 nm range. In this study, calibration models relating Vis-NIR spectra to SSC and firmness were developed based on multi-linear regression (MLR), principal component regression (PCR) and partial least squares regression (PLSR) with respect to the logarithms of the reflectance reciprocal log(1/R), its first derivative D₁ log(1/R) and second derivative D₂ log(1/R). The best combination, based on the robust models and the prediction results, was PLSR method with respect to log(1/R) at equatorial position of pear fruit. The PLSR models for prediction samples resulted correlation coefficient (r_p) of 0.912 and 0.854, and root mean standard error of prediction (RMSEP) of 0.662°Brix and 1.232 N for SSC and firmness, respectively. The results indicate that Vis-NIR spectrometry technique could provide an accurate, reliable and nondestructive method for assessing the internal quality indices of intact pear fruit.     

Hyperspectral Imaging Sensing of Changes in Moisture Content and Color of Beef During Microwave Heating Process

Moisture content (MC) and color are two important quality parameters of beef during microwave heating process. This study examined the effects of microwave heating time (0–75 s) on MC, color, and myoglobins of beef samples. The results showed that heating time significantly influenced the MC, color (L*, a*), and percentage of related myoglobins. The suitability of hyperspectral imaging (HSI) (400–1000 nm) was investigated to correlate the mean spectra of beef samples and the color and MC values during microwave treatment. After the use of pre-processing methods and optimum wavelengths selection, the SG-SPA-LS-SVM prediction model for MC (R²_P?=?0.869, RMSEP?=?1.304, and RPD?=?2.724) and the SG-RC-MLR model for a* (R²_P?=?0.890, RMSEP?=?0.735, and RPD?=?2.733) were established. The models were then used to develop the distribution maps of MC and a* values, respectively, showing that both MC and a* at the center of the meat slices were higher than those at the edge, corresponding to the temperature distribution during microwave heating. The results demonstrated the ability of HSI system for monitoring the changes of some quality parameters during microwave heating.     

Shortwave-near infrared spectroscopy for determination of reducing sugar content during grape ripening,winemaking, and aging of white and red wines

The feasibility of near infrared (NIR) spectroscopy for predicting reducing sugar content during grape ripening, winemaking, and aging was assessed. NIR calibration models were developed using a set of 146 samples scanned in a quartz flow cell with a 50 mm path length in the NIR region (800–1050 nm), in a fiber spectrometer system working in transmission mode. Principal component analysis (PCA), partial least squares (PLS), and multiple linear (MLR) regressions were used to interpret spectra and to develop calibrations for reducing sugar content in grape, must, and wine. The PLS model based on the full spectral range (800–1050 nm), yielded a determination coefficient (r²) of 0.98, a standard error of cross validation (SECV) of 13.62 g/l and a root mean square error of cross validation (RMSECV) of 13.58 g/l. The mathematical model was tested with independent validation samples (n = 48); the resulting values for r², the standard error of prediction (SEP) and the root mean square error of prediction (RMSEP) for the same parameter were 0.98, 10.84, and 12.20 g/l, respectively. The loading weights of latent variables from the PLS model were used to identify sensitive wavelengths. To assess their suitability, MLR models were built using these wavelengths. Wavelength significance was analyzed by ANOVA, and four wavelengths (909, 951, 961, and 975 nm) were selected, setting statistical significance at the 99% confidence level. The MLR model yielded acceptable results for r² (0.92), SEP (19.97 g/l) and RMSEP (20.51 g/l). The results suggest that NIR spectroscopy is a promising technique for predicting reducing sugar content during grape ripening, as well as during the fermentation and aging of white and red wines. Individual fingerprint wavelengths strongly associated with reducing sugar content could be used to enhance the efficacy of this simple, efficient and low-cost instrument.     

Microwave assisted fluidized bed drying of nutmeg mace for essential oil enriched extracts: An assessment of drying kinetics,process optimization and quality

The drying mechanism of the myristicin enriched nutmeg mace has been optimized in a microwave assisted fluidization bed dryer (MWFBD) through multiple linear regression (MLR) and artificial neural network (ANN) modeling. The developed drying technique overcomes the non-uniformity heating problems in microwave heating and prolonged drying in fluidized bed drying. During the novel method of drying selected input variables were drying air temperature (DT) (40–50 °C) and microwave power (MP) (480–800 W) and output variables involving colour, oil yield, and myristicin quantity have been investigated by a continuous air velocity of 5.1 m/s. Six mathematical models about one to four conditions have fitted with an experimental design. Suitable enforcement of such models was evaluated through statistical measures. The coefficient of determination (R²) of MLR varied from 0.89 to 0.98, and the sum of squared error (SSE) varied within 6 × 10⁻³ to 158.18, while R² of ANN fluctuated from 0.82 to 0.95, and the mean squared error (MSE) varied between 0.006 and 0.1450, which shows MLR design performance superior than ANN design. The processing conditions of 48.24 °C DT and 637.431 W MP with a drying time of 1.3 h were identified as optimum conditions with a desirability value of 0.98 to obtain maximum oil yield (13.38%) and good colour (L* (20.83), a* (17.34), b* (8.62)) of nutmug mace. Moreover, no myristicin (5.92%) degradation was observed compared with the sun and convective drying. Among the tested models, page and logarithmic models gave a better prediction of moisture ratio.     

Evaluation of apple texture with contact acoustic emission detector: A study on performance of calibration models

Performance of calibration models for evaluation of apples sensory texture with contact acoustic emission detector (CAED) was studied. For model evaluation and testing, 2500 apples of 19 cultivars were harvested over two seasons. Apples were stored at normal atmosphere (NA), controlled atmosphere (CA) for different periods or were treated with 1-methylcyclopropene (1-MCP) in order to obtain a high variability of texture and fruit maturity. Apples were tested simultaneously in two distinct laboratories. The models were created and validated on averaged values from 10 fruits using simple linear regression, multiple linear regression (MLR) and principal component regression (PCR). Performance statistics of the models were expressed in terms of determination coefficient (R²), root mean square errors of cross validation (RMSECV) or prediction (RMSEP) and ratio of prediction to deviation (RPD). Firmness and total acoustic emission counts were predictors of sensory texture in the models. MLR and PCR models show better performance for prediction of sensory data than simple linear regression models however PCR models show the best results among models tested in this study. Common PCR models for several cultivars allow for successful prediction of hardness (RPD > 2.0), crispness and overall texture (1.5 < RPD < 2.0). The single-cultivar PCR models, constructed on data sets containing 26-39 averaged values, reveal significantly better performance (RPD > 2.0 for most of the cases) than the common PCR models for many varieties.     

Estimation of crispness and consumer acceptance of fried potato chips by mechanical and acoustic measurements

Fried potato chips retaining various moisture contents (MCs) (2.21–9.20%) were analysed to estimate the intensity of crispness and consumer acceptance by texture and acoustic measurements. The MC of the chips was highly correlated with the mechanical maximum force (MMF) in the texture measurement, total area (MTA) and number of sound peaks (NSP) in the acoustic measurement. The intensities of crispness and consumer acceptance decreased as the MC of potato chips increased. For the predictive models established, the combined use of mechanical and acoustic parameters was shown to better predict sensory crispness intensity [R² = 0.975, root mean square error of prediction (RMSEP) = 0.138] and consumer overall liking (R² = 0.966, RMSEP = 0.111) than either parameter alone. Based on the instrumental‐sensory crispness equivalent table established, the estimated values of the MTA were below 71.24, while the NSP should be above 22.81 to meet ‘slightly like’ category of consumer acceptance.     

Preliminary investigations on the effects of ageing and cooking on the Raman spectra of porcine longissimus dorsi

The influence of ageing and cooking on the Raman spectrum of porcine longissimus dorsi was investigated. The rich information contained in the Raman spectrum was highlighted, with numerous changes attributed to changes in the environment and conformations of the myofibrillar proteins.Predictions equations for shear force and cooking loss were developed from the Raman spectra of both raw and cooked pork. Good correlations and standard errors of prediction were obtained for both WB shear force and cooking loss, with the raw and the cooked samples showing almost identical results R² = 0.77, root mean standard error of prediction (RMSEP)% of mean = 12% for shear force; R² = 0.71, RMSEP% of mean = 10% for cooking loss. The Raman spectra were also able to predict the extent of cooking that occurred within the pork (R²_val = 0.94, RMSEP% of range = 5.5%).Raman spectroscopy has considerable potential as a method for non-destructive and rapid determination of pork quality parameters such as tenderness. Raman spectroscopy may provide a means of determining changes during cooking and the extent to which foods have been cooked.     

Prediction of Cadmium content in brown rice using near‐infrared spectroscopy and regression modelling techniques

The feasibility of prediction of cadmium (Cd) content in brown rice was investigated by near‐infrared spectroscopy (NIRS) and chemometrics techniques. Spectral pretreatment methods were discussed in detail. Synergy interval partial least squares (siPLS) algorithm was used to select the efficient combinations of spectral subintervals and wavenumbers during constructing the quantitative calibration model. The performance of the final model was evaluated by the use of root mean square error of cross‐validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficients for calibration set and prediction set (R_c and R_p), respectively. The results showed that the optimum siPLS model was achieved when two spectral subinterval and fifty‐two variables were selected. The predicted result of the best model obtained was as follows: RMSECV = 0.232, R_c = 0.930, RMSEP = 0.250 and R_p = 0.915. Compared with PLS and interval PLS models, siPLS model was slightly better than those methods. These results indicate that it is feasible to predict and screen Cd content in brown rice using NIRS.     

Chemometrics coupled with ultraviolet spectroscopy: a tool for the analysis of variety,adulteration, quality and ageing of apple juices

This study demonstrates the use of UV spectroscopy (UV) in combination with chemometrics as a simple and feasible approach for analysis of variety, adulteration, quality and ageing of apple juice. The results show that PCA‐UV is adequate to differentiate apple juice varieties and adulteration. The percentage of the adulterant can be detected by PLSR‐UV with RMSE < 0.7783% and R² > 0.9980. For the evaluation of juice quality, PLSR‐UV (RMSE = 0.2555–2.3448; R² = 0.7276–0.9816) is recommended for the prediction of soluble solids, ascorbic acid, total flavonoids, total sugar and reducing sugar, whilst PCR‐UV (RMSE = 0.0000–2.7426; R² = 0.7073–1.0000) is adequate for the prediction of pH and antioxidant activity. In addition, PLSR‐UV may be used to predict the storage time with RMSE = 0.4681 day and R² = 0.9832. Therefore, UV coupled with chemometrics has potential to be developed as a portable tool for the detection of variety, adulteration, quality and ageing of not only apple juices, but also other fruit and vegetable juices.     

Hyperspectral imaging as an effective tool for prediction the moisture content and textural characteristics of roasted pistachio kernels

The objective of this study was to develop calibration models for prediction of moisture content and textural characteristics (fracture force, hardness, apparent modulus of elasticity and compressive energy) of pistachio kernels roasted in different conditions (temperatures 90, 120 and 150 °C; times 20, 35 and 50 min and air velocities 0.5, 1.5 and 2.5 m/s) using Vis/NIR hyperspectral imaging and multivariate analysis. The effects of different pre-processing methods and spectral treatments such as normalization [multiplicative scatter correction (MSC), standard normal variate transformation (SNV)], smoothing (median filter, Savitzky–Golay and Wavelet) and differentiation (first derivative, D¹ and second derivative, D²) on the obtained data were investigated. The prediction models were developed by partial least square regression (PLSR) and artificial neural network (ANN). The results indicated that ANN models have higher potential to predict moisture content and textural characteristics of roasted pistachio kernels comparing to PLSR models. High correlation was observed between reflectance data and fracture force (R²?=?0.957 and RMSEP?=?3.386) using MSC, Savitzky–Golay and D¹, compressive energy (R²?=?0.907 and RMSEP?=?15.757) using the combination of MSC, Wavelet and D¹, moisture content (R²?=?0.907 and RMSEP?=?0.179) and apparent modulus of elasticity (R²?=?0.921 and RMSEP?=?2.366) employing combination of SNV, Wavelet and D¹, respectively. Moreover, Vis–NIR data correlated well with hardness (R²?=?0.876 and RMSEP?=?5.216) using SNV, Wavelet and D². These results showed the capability of Vis/NIR hyperspectral imaging and the central role of multivariate analysis in developing accurate models for prediction of moisture content and textural properties of roasted pistachio kernels.     

Development and evaluation of chitosan and its derivative for the shelf life extension of beef meat under refrigeration storage

The current study was aimed to study the shelf life of beef meat at refrigeration storage using novel chitosan derivative (Ch‐D). Chitosan was modified with antimicrobial monomethyl fumaric acid (MFA). The chemical structure of the resulting material was characterized by FT‐IR and HR‐XRD. The results confirmed the successful synthesis of conjugate sample. For the shelf life study, following lots were used: control (distilled water), chitosan 0.5%, Ch‐D 0.5%. The samples were kept at 4 °C for 16 days and analyzed at 4‐day intervals. Ch‐D treatment significantly reduced the total viable counts, enterobacteriaceae, lactic acid bacteria, psychrotrophic bacteria, and yeasts–moulds when compared with the chitosan and control during refrigeration storage. The peroxide value (PV) for Ch‐D was 0.19 ± 0.04 meq O₂ kg^?1 fat and chitosan was 0.21 ± 0.07 meq O₂ kg^?1 fat and showed no significant difference (P < 0.05) at the end of the storage. Thiobarbituric acid reactive substance (TBARs) value for Ch‐D was 0.48 ± 0.02 mg MDA kg^?1 and chitosan was 0.57 ± 0.09 mg MDA kg^?1, while the carbonyl contents for Ch‐D was 3.16 ± 0.23 nm  mg^?1 protein and chitosan was 3.11 ± 0.16 nm  mg^?1 protein at 16 days of storage. Values for Ch‐D and chitosan treatments were significantly lower (P < 0.05) for all the quality attributes as compared with the control throughout storage. At the end of storage, Ch‐D treatment showed a significant increase (P < 0.05) in lightness (L^* = 41.54 ± 0.09) and yellowness (b* = 2.23 ± 0.04). The redness for control was high (a* = 6.12 ± 0.09) as compared with the treated samples. Based primarily on microbial counts, Ch‐D treatment extended the shelf life of beef meat by about 8 days, maintaining acceptable quality attributes.     

Quantitative determination of <Emphasis Type="Italic">Lactobacillus</Emphasis> spp. in milk using a series piezoelectric quartz crystal sensor

A series piezoelectric quartz crystal (SPQC) sensor was developed for quantitative determination of Lactobacillus spp. populations in milk. When the electrodes were immersed in a reaction cell with bacterial inoculum, the change of frequency was caused by the impedance change of the microbial metabolism. A significant frequency decrease was found due to the coagulation of milk when the Lactobacillus spp. was cultivated in the media. The SPQC sensor system established in this study demonstrated the specificity and selectivity for detection of Lactobacillus spp. in milk sample. The calibration curve of detection time against density of Lactobacillus spp. shows a linear correlation coefficient (R ² = 0.8453) over the range of 10²–2.4 × 10⁵ CFU ml⁻¹. The detection time was influenced by the addition of peptone and glucose. The sensor exhibited rapid (within 4 h) and enabled real time monitoring of Lactobacillus spp. growth. This system is potentially applicable to detect Lactobacillus spp. concentration at local farm when a suitable temperature control device is adapted.     

近红外高光谱联用Stepwise算法快速无接触评估冷鲜鸡肉色泽及嫩度

利用900~1700 nm近红外高光谱成像系统联用Stepwise算法快速评估鸡肉色泽和嫩度。通过采集新鲜屠宰鸡肉高光谱图像,提取试验样本感兴趣区域(Region of interests,ROI)反射光谱信息,经中值滤波平滑(Median filtering smoothing,MFS)、多元散射校正(Multiplicative scatter correction,MSC)和标准正态变量变换(Standard normal variable correction,SNV)三种预处理后,分别利用偏最小二乘(Partial Least Squares,PLS)和多元线性回归(Multiple linear regression,MLR)挖掘光谱信息与鸡肉色泽参数(L*、a*、b*)及嫩度参考值之间的定量关系。结果显示,经MFS预处理的近红外光谱(486个波长)构建的全波段PLS回归模型(F-PLS)预测L*(R_P=0.904,RMSEP=2.036)、b*(R_P=0.908,RMSEP=1.577)和嫩度(R_P=0.948,RMSEP=1.596)效果更好。为提高预测效率,采用Stepwise算法筛选最优波长优化F-PLS模型,结果显示,从SNV预处理光谱筛选的14个最优波长构建MLR回归模型预测L*值(R_P=0.894,RMSEP=2.160)效果较优,从SNV预处理光谱筛选的13最优波长构建的O-PLS回归模型预测b*值(R_P=0.877,RMSEP=1.811)效果较优,从MFS预处理光谱筛选的20个最优波长构建O-PLS回归模型预测嫩度值(R_P=0.888,RMSEP=2.408 N)效果较优。本试验表明,利用近红外高光谱成像技术结合Stepwise算法可实现鸡肉色泽参数L*、b*值以及嫩度的快速评估。     

Dietary administration of ewe diets with a distillate from rosemary leaves (Rosmarinus officinalis L.): Influence on lamb meat quality

The effect of including distilled rosemary leaf in the diet of pregnant ewes on subsequent lamb meat quality was studied. Thirty-six Segureña ewes were randomly assigned to three homogeneous groups. One group was fed a basal diet (BD) as control while the diet of the other two groups was modified by substituting 10% (R₁) and 20% (R₂) of the BD with a pellet made from 50% barley and 50% of distilled rosemary leaves (DRL). Meat spoilage (TVC, PSY and MYC), TBARS, CIELab coordinates and the sensory characteristics contribution of fresh lamb meat packed in MAP (70% O₂:30% CO₂) were analyzed on days 0, 7, 14 and 21. In general, R₁ and R₂ had higher a^* values, better scores for meat and fat colour (P < 0.05) and lower TBARS and rancid odour (P < 0.05), than the control samples. The total viable count was lower in meat DRL. No statistically significant differences were detected between the two treatments (10–20% DRL).     

Prediction of moisture content uniformity of microwave-vacuum dried mangoes as affected by different shapes using NIR hyperspectral imaging

The prediction of moisture content uniformity on mango slices as affected by four different shapes (square, rectangle, regular triangle, and round shape) during microwave-vacuum drying (MVD) was investigated using near-infrared hyperspectral imaging in combination with multivariate chemometric analysis. Applying spectral pretreatment of a 2nd derivative followed by mean-center to raw spectra was found to be greatly beneficial for the reduction of noise and scattering levels. Seven wavelengths (951, 977, 1138, 1362, 1386, 1420, and 1440 nm) with larger absolute values of regression coefficients derived from a partial least square regression model were identified as feature variables for moisture prediction. An optimized model based on the selected wavelengths was developed using multivariate linear regression, achieving a high prediction accuracy with R_p² = 0.993 and RMSEP = 1.282%. From the moisture distribution map, a similar non-uniform drying pattern was found on square, rectangle and regular triangle-shaped samples, while round-shaped mango slices achieved better drying results.Industrial relevanceThe current study suggested that NIR hyperspectral imaging was a promising technique in predicting the moisture content of mango slices during MVD, and non-uniformity of moisture distribution and the effect of sample geometry should be taken into account when the microwave-vacuum method is implemented in drying.     

Visible/near-infrared hyperspectral imaging prediction of textural firmness of grass carp (Ctenopharyngodon idella) as affected by frozen storage

Textural firmness is a primary determinant of consumer acceptance for evaluating freshness quality of fish fillet flesh. The objective of this study was to investigate the potential of using visible and near-infrared hyperspectral imaging (400–1000 nm) for non-destructive prediction of firmness quality of grass carp fillet as affected by frozen storage. Fillet samples were frozen at − 20 °C for 24 h and then stored at 4 °C for thawing over five days. Hyperspectral images were obtained at different thawing stages and their corresponding spectral data were extracted. Two calibration models were established between the extracted spectral data and the reference firmness values measured by the traditional mechanical method by using partial least squares regression (PLSR) and least-square support vector machine (LS-SVM) analysis. Three approaches of regression coefficients (RC) from PLSR analysis, genetic algorithm (GA) and successive projection algorithm (SPA) were utilized to recognize the most important wavelengths that possessed the greatest influence and sensitivity on the firmness prediction based upon the whole spectral range. By comparing the above-mentioned three variable selection methods, seven optimal wavelengths (450, 530, 550, 616, 720, 955 and 980 nm) were selected by GA and its corresponding simplified prediction model of GA-LS-SVM was also obtained, showing the best performance with the highest determination coefficient (R²_P) of 0.941 and the lowest root mean square error estimated by prediction (RMSEP) of 1.229. The overall results of this study suggested that hyperspectral imaging technique has a potential for fast and non-destructive prediction and analysis of textural firmness of grass carp fillets as affected by frozen storage.     

Detection and Quantification of Enterobacter sakazakii by Real-time 5′-Nuclease Polymerase Chain Reaction Targeting the palE Gene

Enterobacter sakazakii is an emerging food-borne pathogen causing invasive infection with high mortality rates in neonates and infants. The aim of this study was to develop, optimize, and evaluate real-time 5′-nuclease polymerase chain reaction (PCR) for the specific detection and quantification of E. sakazakii. Original primers and TaqMan probe targeting a sequence of E. sakazakii palE gene were designed. The developed real-time PCR system was highly specific for E. sakazakii with 100% inclusivity determined using 54 E. sakazakii strains and 100% exclusivity determined using 99 other strains. Detection limits of 4 × 10² and 4 × 10¹ CFU ml⁻¹ were determined with 100% and 90% probability, respectively. The response of the 5′-nuclease PCR system was linear (correlation coefficient ≥ 0.997) in the range of 10¹ to 10⁸ CFU ml⁻¹. Five methods of DNA sample preparation were compared. The methods of DNA preparation using the InstaGene Matrix and the simple lysis by boiling with the Triton X-100 were the most sensitive with calibration lines applicable for quantification. The developed real-time PCR targeted to the palE gene provides an alternative possibility for the detection and quantification of E. sakazakii after the suitable sample preparation.     

Predicting the Heating Temperature of Soymilk Products Using Fluorescence Fingerprints

A novel measurement technique using fluorescence fingerprints (FFs) was developed to measure the degree of heat treatment applied to soymilk. FFs are a set of fluorescence spectra acquired at consecutive excitation wavelengths. Soymilk was heated at 50, 60, 70, 80, or 90 °C, for 10 min, and the samples were measured both in the liquid and freeze-dried forms. Partial least squares (PLS) regression models were constructed to predict heating temperature from the FFs of liquid soymilk and freeze-dried soymilk. Heating temperatures were predicted from soymilk FFs with root-mean-square errors of prediction (RMSEP) and R ²P of 7.20 °C and 0.92 and from freeze-dried soymilk FFs with RMSEP and R ²P of 9.00 °C and 0.89, respectively. The fluorescence of aromatic amino acids and Maillard products mainly contributed to the prediction models. FF measurement proved to be effective for the objective control of the soymilk heating process.     

The expression of genes PKM2 and CAST in the muscle tissue of pigs differentiated by glycolytic potential and drip loss, with reference to the genetic group

The present studies aimed at an analysis of the expression level of genes PKM2 and CAST in Longissimus lumborum [LL] muscle tissue of pigs differing as regards the glycolytic potential [GP] and drip loss [DL] from the LL muscle, with reference to the genetic group. The studies covered a total of 65 pigs: 20 purebred Landrace [L], 22 crossbreeds of Landrace with the Yorkshire [L × Y] and 23 three-breed crosses (Landrace × Yorkshire) × Duroc [(L × Y) × D]. In the case of gene PKM2 one may observe in (L × Y) × D crossbreds, compared to L × Y crossbreds, an increased expression, closely related with the increase in dry matter content, including intramuscular fat, as well as a more favourable progress of glycolytic and energy metabolism during the early time post mortem (pH₄₅ and R₁). Compared with Landrace animals, the lower expression of the CAST gene observed in (L × Y) × D pigs is manifested by a marked improvement of meat quality (R₁ pH₄₅ pH₂₄, pH₄₈), arising from the rate of glycolytic and energy metabolism, typical for normal meat, that in effect results in its higher culinary and technological value.