Lu Li,Xuejin Li,Ge Go,Yiming Yn,Xiodong Wng,Yo Tng,Yuqin Jing,*,Xihong Li,*
a State Key Laboratory of Food Nutrition and Safety,Tianjin University of Science and Technology,Tianjin 300457,China
b College of Bioengineering,Tianjin University of Science and Technology,Tianjin 300457,China
Keywords:Fresh extruded rice-shaped kernels Shelf life Kinetic model Rice ageing Predicting
A B S T R A C T Fresh extruded rice-shaped kernels(FER)are remoulded rice products from cereals or seed flours,which have the advantages of safety,nutrition,health and time saving.However,the finished products are easy to react with oxygen,so it is necessary to develop a fast,simple and reliable approach to monitor and predict the shelf-life of FER.A comprehensive mathematical model of FER shelf-life prediction was developed using a dynamic modelling approach based on real supply chain conditions.This predictive model was developed to determine four key indexes including acid value,iodine blue value,water uptake ratio and peroxide value.The results showed that when the peroxide value was 1.6849,the FER lost its edible value,nutritional value and commodity value.Moreover,the acid value and peroxide value of FER were used to establish a first-order kinetic model,and the iodine blue value of FER was suited for a zero-order kinetic model.The validation experiment of predicted and measured shelf life showed that the relative error was 3.12%,which was less than 5%.Therefore,this kinetic model could be used to predict the shelf-life of FER quickly and conveniently.The kinetic-based shelf-life prediction model proposed in this study is rapid and practical,providing theoretical basis and guidance for the establishment of quality monitoring and quality evaluation systems of FER during the production,storage,transport and marketing.
Rice(Oryza sativaL.)is one of the major grain crops all over the world and is cultivated in more than 100 countries[1-3].Rice is rich in carbohydrates,dietary fibres,vitamins,and minerals[4].However,rice is deficient in bioavailable micronutrients to meet daily nutritional requirements,which may lead to an imbalance in nutrition intake if people consume rice as a staple[5].Therefore,it should be a good strategy to mix rice with supplementary substances to obtain fresh extruded rice-shaped kernels(FER),which is a new rice product with high quality and various formulations[6-8].The extrusion process of FER involves heating,kneading,mixing and moulding,during which a variety of components can be easily added to the final FER products[9].As the processing of FER usually consists of breaking,pulverization,and recombination of several components,the oxidizable substances in the raw materials will inevitably react with oxygen in the air,forming new chemical groups that are not conducive to preservation[10].Moreover,the autoxidation will continue during storage,eventually rendering the rice to be inedible.Related studies have found that the inedible rice was mainly characterized by high oxidation and rancidity level,and the human naked eyes can hardly distinguish whether it is edible[11].Hence,it is necessary to develop a quick and convenient method to evaluate the extent of oxidation and rancidity and thus to predict the shelf-life of FER.
Shelf-life prediction models have been widely used,such as fresh fruit and vegetable shelf-life prediction based on respiration rate[12],fresh-cut potato shelf-life prediction based on PPO enzyme activity[13],tomato shelf-life characterization by volatile compounds[14],and so on.However,high starch foods are prone to fat oxidation or hydrolysis of starchprotein-fat[15,16],resulting in reduced quality.Therefore,fatty acid value is one of the vital indicators to evaluate the ageing of rice during storage.Studies have found that fatty acid values are predictable.Li et al.[17]have successfully developed a mathematical prediction model for fatty acid oxidation stability through the Thermogravimetric(TGA)method.Jiang et al.[18]innovatively proposed using rice colour component combination to establish a backpropagation neural networks(BPNN)model for the quantitative detection of fatty acid value during rice storage,the correlation coefficient(Rp)and the prediction root mean square error(RMSEP)were 0.9715 and 0.4310 mg/100 g,respectively.In addition,a partial least square(PLS)regression model was established to achieve rapid monitoring of fatty acid values during rice storage.In the process of prediction,the coefficient of determination(R2)was 0.9615 and the RMSEP was 0.3626[19].The mathematical models for predicting the changes of fatty acid in rice have attracted growing research interests.
In this study,the changes in free fatty acid(FFA),peroxide value(POV),iodine blue value(IBV)and water uptake ratio(WUR)under 4°C,10°C,20°C and 30°C were measured,and FFA,POV,IBV and WUR models were proposed to predict the shelf-life of FER.Mathematical regression was used to analyze the peroxide value and fatty acid value to establish a relationship equation for predicting the complex indicator(FFA)by a simple indicator(POV),and a kineticbased shelf-life prediction method was obtained.In the future,this study is expected to monitor the quality of FER and similar reconstituted food products.
2.1.Materials
Rice flour(Japonica)(12.66% moisture,78.51% total starch,7.80%protein,0.82%dietary fibre,0.65%fat)was purchased from the Yunhe Biotechnology Co.,Ltd.(Shanxi,China).Oat bran powder(12.53%moisture,63.71%starch,11.29%protein,1.63%ash,6.53%fat)was purchased from the Zhangjiakou Xinsu Oat Food Technology Co.,Ltd.(Hebei,China).Corn flour(12.00% moisture,71.33%starch,9.47% protein,2.40% crude fat,4.19% ash)was purchased from Xinghua Lvshuai Food Co.,Ltd.(Zhejiang,China).All of the samples were dried and ground with a high-speed grinder(XL-10B,Xulang Machinery,Guangdong,China)at 25,000 r/min for 5 min,screened by a 120-mesh sieve,and stored at-18°C for further use.
2.2.Experimental treatment
In the pre-experiment,rice,oat bran,and corn flour were mixed in a mass ratio of 6:3:1.Then,the mixed powder was mixed uniformly with water at a weight ratio of 100:20 by an industrial mixer(HWT20,Jinan Sanguan Food Machinery Co.,Ltd.,Shandong,China)for 10 min.Subsequently,the sample was held at room temperature for 1 h to achieve water balance.Finally,extrusion processing were conducted by means of a twin-screw extruder(SLG32-II,Jinan Shengrun Technology Development Co.,Ltd.,Shandong,China)to obtain FER samples.The screw of the extruder was divided into three zones with different temperatures and different speeds,i.e.feed zone(80 °C,10 r/min),screw zone(120°C,8 r/min),and cutting zone(90°C,41 r/min),respectively.The obtained FER samples were dried at 45°C for 5 h to achieve a final moisture content of 13.0%-14.5%,followed by storing at a constant temperature and humidity cabinet(Shanghai Jianheng Instrument Co.,Ltd.,China)with humidity ranging from 60% to 65% and temperature of 4 °C,10 °C,20 °C and 30 °C respectively.Samples were collected every 7 d to determine the indexes including FFA,POV,IBV and WUR.
2.3.Chemical analysis
The moisture content was determined according to the method of Nielsen[20].The POV was determined based on the method described by Park and Kim[21],and FFA was determined according to the American standard method in AACC 02-01a(1999).The IBV and WUR of cooked FER were determined according to the method described by Li and Shu[22].
2.4.Model establishment and verification
In the experiment,the linear and nonlinear variation data for each quality indicator were fitted by the mathematical and statistical analysis software(Origin 2018).The fitting curves of zero-order and first-order reaction rate constants(k)corresponding to the change in each indicator were obtained.The zero-order reaction rate constant was the slope of the linear fitting,and the first-order reaction rate constant was the slope of the logarithm of the measured values of each indicator and time curve.According to the value ofR2,a dynamic model with a high fitting degree of each indicator was selected.With lnkas the ordinate and 1/Tas the abscissa,a linear fitting was obtained as the relationship curve between lnkand 1/Twas plotted[23,24].The Arrhenius constant(A)and the Arrhenius activation energy(Ea)can be respectively determined by the intercept and slope of a straight line,the slope of the straight line isEa/R,and the intercept value of the straight line is lnA.
The indicators obtained in the experiments were fitted to obtain a zero-order reaction model equation(Eq.1)and a first-order reaction model equation(Eq.2).When combined with the Arrhenius equation,a zero-order kinetic prediction model(Eq.3)and a first-order dynamics prediction model(Eq.4)were concluded.
whereyis the value of quality attributes at storage timet;y0is the initial value of quality attributes;kis the quality change rate constant;tis the storage time,d;Ais the reaction rate constant;Eais the activation energy,kJ/mol;Ris the gas constant 8.314 J·(mol·K)-1;Tis the storage temperature,K.
Furthermore,a verification model is determined by calculating the mean relative percentage error(w).When the value ofwis less than 10%,it was considered that the established quality model was in accordance with dynamic change[25].The verification model of FER was established as follows.
whereVєis the measured value of each test,Vөis the predicted value for each test,andNis the test times.
Finally,the correlation analysis was carried out on all measured indexes by using FFA to determine the index with the best correlation,and then the change relationship factors are transformed according to the dynamic model constants.The relationship equation(Eq.6),for predicting the shelf-life of FER in different storage temperatures,was obtained.
whereHis the shelf-life,d;Ftis the value corresponding to the physical and chemical indexes at the end of the shelf-life of FER;Tis the storage temperature,K.
2.5.Statistical analysis
All experimental data were presented as the mean±standard deviation of three replicates,and all statistical analyses were performed with SPSS 20.0(SPSS Inc.,Chicago,USA).In addition,Duncan"s multiple tests were applied to verify significant differences between the predicted and measured values of physical and chemical indexes of FER at a level ofP＜0.05.
3.1.Effect of storage temperature on FFA,POV,IBV and WUR
Determined by the classical iodine-based colourimetry,IBV is based on the interaction of the helical amylose chains with iodine to form a blue complex,as amylopectin becomes purple-red when it meets iodine,which could reflect the degradation of pullulan during storage and therefore indirectly evaluate FER quality[26].POV is the optimal index to determine the autoxidation(oxidative rancidity)test[27].Peroxides are intermediates in the autoxidation reaction.Generally speaking,a higher peroxide value indicates higher peroxide content,suggesting higher rancidity of FER.As shown in Fig.1,the IBV and WUR gradually decreased,while the FFA and POV increased with the storage time of 28 d.Moreover,FER stored under higher temperatures showed higher FFA.During the storage,moisture,mildew and lipase participated in hydrolysis,which increased the content of free fatty acids in FER.The POV reached the peak value on day 28 when the ambient temperature was 30°C,indicating the poorest FER quality.
3.2.Data fitting analysis of FFA,POV,IBV,WUR
Zero-order and first-order kinetic models were used to build the kinetic model of food quality.The Arrhenius formula showed that lnkhad a linear relationship with 1/T,and the first-order model fitted the changes in FFA and POV better than the zero-order model,based on the value ofR2shown in Table 1.According to Table 1,the sum of decision coefficient(ΣR2)of the first-order kinetic model of FFA,POV,IBV,and WUR were 2.9418,2.8421,2.7974,and 2.8083,respectively.On the other hand,the coefficient sum of theR2of the zero-order kinetic model were 2.8743,2.8227,2.8384,and 2.8326,respectively.Comparing the sum ofR2of the first-order and zero-order kinetic models for each index,it was concluded that the value of the firstorder kinetic models for FFA and POV were greater than those of the zero-order kinetic models,while IBV and WUR showed opposite results.Therefore,the first-order kinetic model was established based on the measured values of FFA and POV,while the zero-order kinetic model was established based on IBV and WUR.
Table 1Rate constant(k)and decision coefficient(R2)of zero-order and first-order kinetic equations.
3.3.Prediction model establishment
Fig.1.Effect of temperatures on FFA(A),IBV(B),POV(C)and WUR(D)of FER.
It could be concluded from Fig.2A that the regression equation between the logarithmic of FFA rate constant(lnk)and the reciprocal of temperature(1/T)isy=64.7929x-0.24210 with the correlation coefficient of 0.9987,which showed that the first-order kinetic model is well combined with Arrhenius formula.The activation energy(Ea=538.65 kJ/mol)and Arrhenius constant(A=1.2739)were calculated by the Arrhenius equation.For this reason,a fast FFA prediction model was established(Eq.7).
whereQis the FFA of FER at the selected storage time,mg/100 g;Q0is the initial FFA of FER,mg/100 g.
According to Fig.2B,the zero-order kinetic regression equation of IBV wasy=-153.2402x+0.47057,and the correlation coefficient was 0.9903.The activation energyEa=1274.04 kJ/mol andA=1.6009 was calculated by the Arrhenius equation.Therefore,the IBV prediction model was established(Eq.8).
whereWwas the IBV of FER at the selected storage time,W0was the initial IBV of FER.
According to Fig.2C,the first-order kinetic regression equation of POV wasy=90.2127x-0.36387,and the correlation coefficient was 0.9326.The activation energyEa=750.03 kJ/mol andA=1.4389 was calculated by the Arrhenius equation.For this reason,the POV prediction model was established(Eq.9).
Fig.2.Linear fitting analysis diagram of FFA(A),IBV(B),POV(C),WUR(D).
whereRis the POV of FER at selected storage time,meq/kg;R0is the initial POV of FER,meq/kg.
From Fig.2D,the regression equation obtained from the logarithmic value of the WUR constant(lnk)and the corresponding reciprocal temperature(1/T)wasy=-70.6990x+0.18520,with the correlation coefficient of 0.2758(＜0.9000),which did not reach interconnection with the Arrhenius equation.Therefore,we do not recommend the WUR index for model forecast.
3.4.Verification of the prediction model
The FFA,POV,and IBV of FER stored at 10°C were calculated according to the corresponding prediction models,and then compared with the measured values to verify the models.As shown in Table 2,the regression coefficients between the predicted value and measured value of the three indicators were higher than 0.9000,and the mean relative error of each index was lower than 10%,which indicatedthat the prediction models were effective and excellent in predicting the shelf-life of FER.
Table 2Dynamic model equation verification.
Table 3 showed the correlation between FFA and POV,FFA and WUR at different temperatures.As shown in Table 3,FFA was substantially in significant correlation(P＜0.05)with the other three indicators at all temperatures,indicating that the model has high fitting accuracy[28].Nonetheless,there was no correlation between FFA and IBV values(P＞0.05)at 10 °C.Therefore,the optimal model was selected by comparing the regression coefficients based on the Pearson correlation of each index model with temperature(4 °C,10 °C,20 °C,30 °C).In Table 3,the regression coefficients of POV showed the highest value,indicating that POV was the optimal index for predicting the shelf-life of FER.
Table 3Correlation analysis between free fatty acids(FFA)and other indicators.
In addition,the Arrhenius model for FER quality prediction was fitted based on the above-mentioned equations.Based on the Arrhenius model,the Arrhenius constant(A)at 10°C was calculated and the shelf-life was predicted by the POV model.Finally,the model equation of shelf-life prediction can be derived as follows.
whereyis the shelf-life,d;Ptis POV of FER during storage;P0is the POV of FER at the initial stage of storage;andTis the absolute thermodynamic temperature,K.
And incorporate those values ofA,EaandRinto Eq.(11)to obtain the shelf life prediction model of FER.
3.5.Validation of shelf life prediction model for FER
Fatty acid value(KOH/dry basis)is an essential indicator to evaluate FER quality.Chinese Official Analysis Methods andRice Quality Judgment Rules(ISO7350:1998(E)Milled cereal products and GB/T 20569-206)stipulate that the fatty acid value of rice should be lower than 25 mg/100 g during storage.When the content exceeds this value,FER undergoes oxidation and rancidity,rapidly deteriorates in quality,and is difficult to store,which is considerable for its shelf-life prediction.A mathematical regression analysis of FFA and POV was performed using software to obtain the regression equation between FFA and POV(Fig.3).In Fig.3,there was a linear regression relationship between FFA and POV.When the FFA is 25 mg/100 g,the POV will reach 1.6849,which is a sign of ending shelf-life.In this study,according to the shelf-life prediction model(Eq.10),the predicted shelf-life of FER stored at 10 °C was 52 d(the derived value was 51.56),while the actual shelf-life was 50 d.The relative error between predicted and actual shelf-life was 3.12%,which was lower than the standard range[29].
Fig.3.Regression analysis of POV and FFA.
The kinetic model was practical and therefore widely used in cold chain logistics food such as fruits,vegetables and grains.In this study,a kinetic model for predicting shelf-life of fresh extruded rice-shaped kernels was established to not only evaluate the predicted values of FFA,POV,and IBV of FER at a specific storage temperature(4-30 °C)for a specific storage time,but also predict the shelf-life of FER and ensure the relative error is within the standard error range.This model provides a faster and more accurate method for predicting the shelf-life of FER,which deserves further investigation and application.Temperature was the primary influence in the cold chain process and both storage temperature and temperature fluctuations affect the chemical reaction system,so further research will be conducted on temperature fluctuations of±5 °C,±1 °C,±0.5 °C and±0.1 °C in the future to bring predicted values closer to actual values.
Author Contributions
Lu Li:Conceptualization,Investigation,Software,Writing-original draft;Xuejin Li:Writing-review&editing;Ge Gao:Writing-review&editing;Yiming Yan:Methodology,Resources;Xiaodong Wang:Data curation;Yao Tang:Methodology;Yuqian Jiang:Funding acquisition,Supervision;Xihong Li:Funding acquisition,Supervision.
Conflicts of Interest
The authors declared that they have no conflicts of interest in this work.
Acknowledgements
This study was supported by a grant from the National Key Research and Development Program of China(2017YFD0401305)and the Key Research and Development Program of Shandong Province(2021CXGC010809).
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