Impact damage in mangosteens (Garcinia Mangostana L.)

Abstract

Mangosteen pericarp is very susceptible to mechanical force. The damaged pericarp releases latex which causes pericarp hardening, spoils the flesh and shortens the shelf life. In order to maintain good quality, the mangosteens require careful handling in every step from the orchards to the consumer. It is essential to establish the limit of mechanical force or energy allowed in the handling processes. In this study, impact damage in mangosteens was investigated. It was found that the degree of damage can be related to the impact velocity, impact energy and energy absorbed by the fruit. The degree of damage was presented in the terms of the mass of hardened pericarp, damage depth ratio of the pericarp, weight loss and withering of the aril. Two types of packaging materials, the polystyrene foam and card board, were tested for their protective abilities. It was found that the packaging material for the mangosteens should be soft and have low stiffness. Deterioration of damaged mangosteens with storage time was also presented. Empirical equations describing the impact damage behaviour and the allowable impact energy were established.     

Probabilistic modeling of Saccharomyces cerevisiae inhibition under the effects of water activity, pH, and potassium sorbate concentration

Probabilistic microbial modeling using logistic regression was used to predict the boundary between growth and no growth of Saccharomyces cerevisiae at selected incubation periods (50 and 350 h) in the presence of growth-controlling factors such as water activity (a(w); 0.97, 0.95, and 0.93), pH (6.0, 5.0, 4.0, and 3.0), and potassium sorbate (0, 50, 100, 200, 500, and 1,000 ppm). The proposed model predicts the probability of growth under a set of conditions and calculates critical values of a(w), pH, and potassium sorbate concentration needed to inhibit yeast growth for different probabilities. The reduction of pH increased the number of combinations of a(w) and potassium sorbate concentration with probabilities to inhibit yeast growth higher than 0.95. With a probability of growth of 0.05 and using the logistic models, the critical pH values were higher for 50 h of incubation than those required for 350 h. With lower a(w) values and increasing potassium sorbate concentration the critical pH values increased. Logistic regression is a useful tool to evaluate the effects of the combined factors on microbial growth.     

Integration of statistical inferences and machine learning algorithms for prediction of metritis cure in dairy cows

The study's objectives were to identify cow-level and environmental factors associated with metritis cure to predict metritis cure using traditional statistics and machine learning algorithms. The data set used was from a previous study comparing the efficacy of different therapies and self-cure for metritis. Metritis was defined as fetid, watery, reddish-brownish discharge, with or without fever. Cure was defined as an absence of metritis signs 12 d after diagnosis. Cows were randomly allocated to receive a subcutaneous injection of 6.6 mg/kg of ceftiofur crystalline-free acid (Excede, Zoetis) at the day of diagnosis and 3 d later (n = 275); and no treatment at the time of metritis diagnosis (n = 275). The variables days in milk (DIM) at metritis diagnosis, treatment, season of the metritis diagnosis, month of metritis diagnostic, number of lactation, parity, calving score, dystocia, retained fetal membranes, body condition score at d 5 postpartum, vulvovaginal laceration score, the rectal temperature at the metritis diagnosis, fever at diagnosis, milk production from the day before to metritis diagnosis, and milk production slope up to 5, 7, and 9 DIM were offered to univariate logistic regression. Variables included in the multivariable logistic regression model were selected from the univariate analysis according to P-value. Variables were offered to the model to assess the association between these factors and metritis cure. Additionally, the univariate logistic regression variables were offered to a recursive feature elimination to find the optimal subset of features for a machine learning algorithms analysis. Cows without vulvovaginal laceration had 1.91 higher odds of curing of metritis than cows with vulvovaginal laceration. Cows that developed metritis at >7 DIM had 2.09 higher odds of being cured than cows that developed metritis at ≤7 DIM. For rectal temperature, each degree Celsius above 39.4°C led to lower odds to be cured than cows with rectal temperature ≤39.4°C. Furthermore, milk production slope and milk production difference from the day before to the metritis diagnosis were essential variables to predict metritis cure. Cows that had reduced milk production from the day before to the metritis diagnosis had lower odds to be cured than cows with moderate milk production increase. The results from the multivariable logistic regression and receiver operating characteristic analysis indicated that cows developing metritis at >7 DIM, with increase in milk production, and with a rectal temperature ≤39.40°C had increased likelihood of cure of metritis with an accuracy of 75%. The machine learning analysis showed that in addition to these variables, calving-related disorders, season, and month of metritis event were needed to predict whether the cow will cure or not from metritis with an accuracy ≥70% and F1 score (harmonic mean between precision and recall) ≥0.78. Although machine learning algorithms are acknowledged as powerful tools for predictive classification, the current study was unable to replicate its potential benefits. More research is needed to optimize predictive models of metritis cure.     

Consumer acceptability and purchase intent of probiotic yoghurt with added glucose oxidase using sensometrics,artificial neural networks and logistic regression

This research aimed to identify the drivers of acceptance and purchase intent of a probiotic (Bifidobacterium longum BL05) nonflavoured yoghurt supplemented with glucose oxidase, and to model the consumers’ acceptability using sensometrics and artificial neural networks (ANN). Consumers (n = 100) evaluated the degree of liking of yoghurt assays in respect of appearance, aroma, taste, texture and overall linking. Sensometric techniques – multiple linear regression (MLR), partial least squares regression (PLS), principal component regression (PCR) – and ANN were used to model the overall liking. Sensory drivers of global acceptance and purchase intent were also determined using logistic regression (LR). Hierarchical cluster analysis (HCA) identified three consumer segments that presented differences in all sensory attributes evaluated (P < 0.05). The ANN model showed the best performance to predict overall liking, followed by the MLR, PLS and PCR, indicating that taste and texture were the most significant attributes impacting the yoghurts overall liking. In accordance with the logistic models, overall acceptance and purchase intent could be predicted with 81.94 and 85.49% accuracy, respectively. The logistic regression indicated that taste was the attribute that contributed significantly (P < 0.0001) to higher scores for purchase intent and was considered the driver of acceptance.     

Determination of translucent content in mangosteen by means of near infrared transmittance

Translucent flesh disorder is undesirable in mangosteen meant for export. However, mangosteens are judged as translucent when the translucent flesh is visible on the pulp surface regardless of the quantity of the internal translucent flesh which may result in some mangosteen assessed as normal having the same amount of translucent flesh content as a mangosteen judged as translucent. The critical amount of translucent flesh to be visible on the pulp surface needs to be determined for assessment purposes. A non-destructive technique to measure the translucent content is a practical tool as the first step towards the establishment of the critical value.A non-destructive model was developed to estimate the translucent content in mangosteens using near infrared transmittance. The translucent area of the flesh section on the fruit surface was used to indicate the translucent content. The effects of the orientation of the fruit and also of the light source to the relative position of the detector as well as the effect of the measurement position of the fruit on the predictive performance were examined. The results showed that the best partial least squares model was achieved with spectra acquired from the fruit position which revealed the largest flesh segment (prediction correlation coefficient was 0.86 and root mean square error of prediction was 7.58%). The horizontal stem-calyx fruit axis and a 135° angle from the light source relative to the detector were the optimal fruit orientation and configuration for measurement.     

Postharvest Damage of Mangosteen and Quality Grading Using Mechanical and Optical Properties as Indicators

Postharvest damage in fresh mangosteens at wholesale level in Thailand was investigated from April to October 2004. A total of 37.1% of the production yield was rendered inedible by damage during this period; damages included fruit cracking, hardened rinds, rough surfaces, translucent flesh, gummosis and decay. This study focused on a method of predicting damage based on the color of the skin of the affected mangosteen. As a first step, diameter, height, weight, and volume of large, medium, small, and undersize mangosteens were measured. The term, dimension ratio, was introduced as a sizing parameter identifying conventional trade size. The coefficient of static friction of the glossy- and rough-surface mangosteens on plexiglass, plywood, and galvanized steel sheet varied from 0.31 to 0.46. The color of sound and defective fruits was measured in terms of their tristimulus values X, Y, and Z. The corresponding chromaticity coordinates of a mangosteen, x and z, depended on the maturity stage of the fruit while y depended on the type of fruit surface. A ratio was proposed to test the accuracy of predicting internal defects from the color variation between two spots on the surface of the same fruit. The highest percentage of correct prediction was 67.4% with a color ratio of X₁ (pink blush color on yellow ground color) to X₂ (pink color) that was greater than 1.25.     

基于机器视觉的香蕉果肉缺陷预测方法

针对香蕉内部果肉缺陷难以预测的问题,运用机器视觉技术对香蕉果皮与果肉进行图像识别,对识别参数进行数据拟合得到果肉缺陷的预测模型。将采集到的图像灰度化并进行滤波去噪,通过双阀值二值化和形态学分析对图像进行识别处理,提取香蕉果皮、香蕉果肉、香蕉果皮黑斑与香蕉果肉缺陷。计算提取区域的像素点总数,将其作为区域面积。分别用香蕉果皮总面积/香蕉果肉总面积与果皮黑斑面积/果肉缺陷面积之比来定义香蕉果皮黑斑度与果肉缺陷度。运用多项式拟合法,根据训练样本得出果肉缺陷预测函数,对预测函数进行残差分析。通过预测模型对香蕉划分等级,总准确率达到88.9%,与通过香蕉果皮进行等级划分其他方法相比,试验所得模型的预测准确率较高,表明通过香蕉果肉进行预测的方法具有一定的优越性。     

蜡样芽胞杆菌生长/非生长界面模型的建立和评价

蜡样芽胞杆菌是软烤虾仁产品的主要变质菌,它是一种条件致病菌,通过产生腹泻毒素和呕吐毒素导致食物中毒。该研究旨在建立一种概率模型来预测出蜡样芽胞杆菌的生长/非生长情况或者生长概率。用lo-gistic回归模型建立不同温度、水分活度和pH环境因子作用下蜡样芽胞杆菌的生长/非生长界面模型。实验结果表明蜡样芽胞杆菌在脑心浸液肉汤培养基中生长的最低温度为9.99℃,最低水分活度为0.931,最小pH值为4.5。在此基础上建立的蜡样芽胞杆菌生长/非生长界面模型的χ2=49.73,P<0.000 1。用logistic回归模型建立的生长/非生长模型拟合效果达到极显著水平。模型的预测值同时很好地量化了环境因子对蜡样芽胞杆菌的协同作用,为软烤虾仁产品中蜡样芽胞杆菌的生长/非生长界面模型的建立提供了参考。     

Use of a Monte Carlo analysis within a physiologically based pharmacokinetic model to predict doxycycline residue withdrawal time in edible tissues in swine

The pharmacokinetics of doxycycline were studied following a single intravenous (I.V.) and intramuscular (I.M.) injection of 10 mg/kg into eight healthy pigs. The steady-state tissue/plasma partition coefficients were obtained via a 3-h constant rate infusion (CRI) in four pigs. Based on the results of in vivo studies and the parameters derived from published work, a physiologically based pharmacokinetic (PBPK) model was developed to predict the drug concentration in edible tissues. The predicted values were then compared with those derived from a previous study. To account for individual differences in the processes of drug metabolism and/or diffusion, a Monte Carlo (MC) run of 1000 simulations was incorporated into the PBPK model to predict the doxycycline residue withdrawal times in edible tissues in swine. The withdrawal periods were compared with those derived from linear regression analysis. The PBPK model presented here provided accurate predictions of the observed concentrations in all tissues except for the injection site. The withdrawal times in all edible tissues derived from the MC analysis were longer than those from linear regression analysis. Based on the residues in the injection site and muscle tissue, the MC analysis predicted a withdrawal time of 33 days. Here, we illustrate that MC analysis can be incorporated into the PBPK model to accurately predict doxycycline residue withdrawal time in edible tissues in swine.     

Einfluß der Extrusionsparameter auf funktionelle Eigenschaften von Weizenstärke

The Influence of Extrusion Parameters on the Functional Properties of Wheat Starch. The functional properties of native starch can be altered by regulating the technical parameters in the extrusion cooking process. The influence of specific variables on the resultant starch properties can be differentiated by means of system analysis. To make this clear, a model has been put forward which divides individual factors into those which influence the process and those which are influenced by the process, as well as examining their interrelationships. As a result of the experiments, it was possible to use regression analysis to examine the relationships between the various system analysis components. Thus it is possible either to predict the properties of the resultant starch knowing the extrusion and system parameters, or to suggest suitable extrusion conditions in order to obtain a particular starch product.     

Defining the growth/no-growth interface for Listeria monocytogenes in Mexican-style cheese based on salt, pH, and moisture content.

The objective of this study was to define combinations of pH, salt, and moisture that produce growth, stasis, or inactivation of Listeria monocytogenes in Mexican-style cheese. A soft, directly acidified, rennet-coagulated, fresh cheese similar to Mexican-style cheese was produced. The cheese was subsequently altered in composition as required by the experimental protocol. A factorial design with four moisture contents (42, 50, 55, and 60%), four salt concentrations (2.0, 4.0, 6.0, and 8.0% wt/wt), six pH levels (5.0, 5.25, 5.50, 5.75, 6.0, and 6.5), and three replications was used. Observations of growth, stasis, or death were obtained for each combination after 21 and 42 days of incubation at 10 degrees C. Binary logistic regression was used to develop an equation to determine the probability of growth or no growth for any combination within the range of the data set. In addition, ordinal logistic regression was used to calculate proportional odds ratios for growth, stasis, and death for each treatment combination. Ordinal logistic regression was also used to develop equations to determine the probability of growth, stasis, and death for formulations within the range of the data set. Models were validated with independently produced data. Of 60 samples formulated to have a 5% probability of Listeria growth (pH, 5.0 to 6.0; brine concentration, 8.17 to 16.00%), none supported growth. Of 30 samples formulated to have 50% probability of growth using the binary model (pH, 5.50 to 6.50; brine concentration, 3.23 to 12.50%), 20 supported growth. Of 30 samples formulated to have a 50% probability of growth according to the ordinal model (pH, 5.50 to 6.50; brine concentration, 3.37 to 10.90%), 16 supported growth. These data indicate that the logistic regression models presented accurately predict the behavior of L. monocytogenes in Mexican-style cheese.     

Can arsenic occurrence rates in bedrock aquifers be predicted?

A high percentage (31%) of groundwater samples from bedrock aquifers in the greater Augusta area, Maine was found to contain greater than 10 μg L(-1) of arsenic. Elevated arsenic concentrations are associated with bedrock geology, and more frequently observed in samples with high pH, low dissolved oxygen, and low nitrate. These associations were quantitatively compared by statistical analysis. Stepwise logistic regression models using bedrock geology and/or water chemistry parameters are developed and tested with external data sets to explore the feasibility of predicting groundwater arsenic occurrence rates (the percentages of arsenic concentrations higher than 10 μg L(-1)) in bedrock aquifers. Despite the under-prediction of high arsenic occurrence rates, models including groundwater geochemistry parameters predict arsenic occurrence rates better than those with bedrock geology only. Such simple models with very few parameters can be applied to obtain a preliminary arsenic risk assessment in bedrock aquifers at local to intermediate scales at other localities with similar geology.     

基于近红外光谱的条斑紫菜菌落总数快速检测技术

为了探究快速、无损地检测条斑紫菜质量的可行性,本研究开发了一种基于近红外光谱技术的条斑紫菜微生物污染程度的定量分析方法。首先对来自不同海域的紫菜样本的菌落总数进行了测定,然后采集了155组样本的原始光谱信息和菌落总数信息。用标准正态变量变换（SNV）、多元散射校正（MSC）、二阶导数（Second-order derivative）等方法对光谱数据进行预处理。在完成最佳预处理方法筛选后,建立了基于光谱信息的非线性拟合（MLR）、支撑向量回归（SVR）、人工神经网络（ANN）、卷积神经网络（CNN）菌落总数预测模型。结果表明,标准正态变量变换与二阶导数的组合预处理效果最优,基于全波段下深度学习模型CNN预测效果最好（r值为0.940）。由此说明,CNN作为一种深度学习模型,可以实现针对条斑紫菜微生物品质的快速评价。     

Texture analysis of fish fillets and minced fish by both sensory and instrumental methods

In an attempt to establish objective criteria for texture analysis as an effective substitute for organoleptic procedures, correlation between the results obtained by a panel of tasters and physical and chemical analyses of texture was determined. In addition, regression equations relating instrumental and sensory methods were applied to predict sensory data from those obtained during instru mental analysis.     

Modeling the Growth/No-Growth Interface of Zygosaccharomyces bailii in Mango Puree

Probabilistic microbial modeling using logistic regression was used to predict critical temperatures to inhibit for at least 35 d Zygosaccharomyces bailii growth in a pH 3.5 mango puree formulated with 1000 ppm of potassium sorbate (KS) or sodium benzoate (NaB) at selected a_w (0.99, 0.98, or 0.97). The probability of growth was calculated, thereby ascertaining the set conditions and critical temperatures required inhibiting yeast growth for different storage times. Using the logistic model, with a growth probability of 0.05, critical temperatures were higher for KS than for NaB. Use of KS to inhibit Z. bailii growth enabled for mango puree 30 d of storage at 6.4 °C.     

基于计算机图像分析的肌内脂肪含量测定

开发了一种快捷、准确的方法对肌内脂肪含量进行了测定。在Matlab操作平台下，应用计算机图像分析方法对大理石花纹含量的特征进行了提取；对与肌内脂肪含量相关性较强的理化指标，如：固体电导率、剪切力值、肌内干物质、灰分等进行了研究。并应用多元线性回归、非线性回归和神经网络等三种不同的数学方法，对肌内脂肪含量进行计算测定。其中，非线性回归模型正确率达到85％以上。     

A probability of growth model for Escherichia coli O157:H7 as a function of temperature, pH, acetic acid, and salt.

Data accumulated on the growth of Escherichia coli O157:H7 in tryptic soy broth (TSB) were used to develop a logistic regression model describing the growth-no growth interface as a function of temperature, pH, salt, sucrose, and acetic acid. A fractional factorial design with five factors was used at the following levels: temperature (10 to 30 degrees C), acetic acid (0 to 4%), salt (0.5 to 16.5%), sucrose (0 to 8%), and pH (3.5 to 6.0). A total of 1,820 treatment combinations were used to create the model, which correctly predicted 1,802 (99%) of the points, with 10 false positives and 8 false negatives. Concordance was 99.9%, discordance was 0.1%, and the maximum rescaled R2 value was 0.927. Acetic acid was the factor having the most influence on the growth-no growth interface; addition of as little as 0.5% resulted in an increase in the observed minimum pH for growth from 4.0 to 5.5. Increasing the salt concentration also had a significant effect on the interface; at all acetic acid concentrations, increasing salt increased the minimum temperature at which growth was observed. Using two literature data sets (26 conditions), the logistic model failed to predict growth in only one case. The results of this study suggest that the logistic regression model can be used to make conservative predictions of the growth-no growth interface of E. coli O157:H7.     

A hybrid Bayesian-neural network approach for probabilistic modeling of bacterial growth/no-growth interface

A hybrid probabilistic modeling approach that integrates artificial neural networks (ANNs) with statistical Bayesian conditional probability estimation is proposed. The suggested approach benefits from the power of ANNs as highly flexible nonlinear mapping paradigms, and the Bayes' theorem for computing probabilities of bacterial growth with the aid of Parzen's probability distribution function estimators derived for growth and no-growth (G/NG) states. The proposed modeling approach produces models that can predict the probability of growth of targeted microorganism as affected by a set of parameters pertaining to extrinsic factors and operating conditions. The models also can be used to define the probabilistic boundary (interface) between growth and no-growth, and as such can define and predict the values of critical parameters required to keep a desired pre-specified bacterial growth risk in check. A modular system incorporating the various computational modules was constructed to illustrate the application of the hybrid approach to the probabilistic modeling of growth of pathogenic Escherichia coli strain as affected by temperature and water activity. The proposed approach was compared to other techniques including the traditional linear and nonlinear logistic regression. Results indicated that the hybrid approach outperforms the other approaches in its accuracy as well as flexibility to extract the implicit interrelationships between the various parameters. Advantages and limitations of the approach were also discussed and compared to those of other techniques.     

Comparing regression,naive Bayes,and random forest methods in the prediction of individual survival to second lactation in Holstein cattle

In this study, we compared multiple logistic regression, a linear method, to naive Bayes and random forest, 2 nonlinear machine-learning methods. We used all 3 methods to predict individual survival to second lactation in dairy heifers. The data set used for prediction contained 6,847 heifers born between January 2012 and June 2013, and had known survival outcomes. Each animal had 50 genomic estimated breeding values available at birth and up to 65 phenotypic variables that accumulated over time. Survival was predicted at 5 moments in life: at birth, at 18 mo, at first calving, at 6 wk after first calving, and at 200 d after first calving. The data sets were randomly split into 70% training and 30% testing sets to evaluate model performance for 20-fold validation. The methods were compared for accuracy, sensitivity, specificity, area under the curve (AUC) value, contrasts between groups for the prediction outcomes, and increase in surviving animals in a practical scenario. At birth and 18 mo, all methods had overlapping performance; no method significantly outperformed the other. At first calving, 6 wk after first calving, and 200 d after first calving, random forest and naive Bayes had overlapping performance, and both machine-learning methods outperformed multiple logistic regression. Overall, naive Bayes has the highest average AUC at all decision points up to 200 d after first calving. Random forest had the highest AUC at 200 d after first calving. All methods obtained similar increases in survival in the practical scenario. Despite this, the methods appeared to predict the survival of individual heifers differently. All methods improved over time, but the changes in mean model outcomes for surviving and non-surviving animals differed by method. Furthermore, the correlations of individual predictions between methods ranged from r = 0.417 to r = 0.700; the lowest correlations were at first calving for all methods. In short, all 3 methods were able to predict survival at a population level, because all methods improved survival in a practical scenario. However, depending on the method used, predictions for individual animals were quite different between methods.     

Box-behnken设计优化富硒酵母培养条件参数的研究

以糙米汁、麦芽汁和豆芽汁为天然培养基,研究了培养条件对菌体及总硒产量的影响,采用Box-be- hnken设计对富硒酵母培养条件进行了优化。经过回归分析建立了总硒产量对培养条件的二次回归模型,其回归方程的决定系数达到0.997。得到的优化培养条件为培养温度为27.43℃、pH值为5.78、装液量为89.73 mL,总硒产量最大预测值达到4.48 mg/L,是优化前的1.27倍。