Research article

Influence of delayed cooling on the quality of tomatoes (Solanum lycopersicum L.) stored in a controlled chamber

  • Received: 21 March 2020 Accepted: 15 June 2020 Published: 18 June 2020
  • Quality degradation due to inappropriate postharvest handling, including short exposure to high and variable temperature environments and cooling delay, is a critical issue for maintaining the freshness of vegetables and fruits in further marketing chains and final consumption. The goal of this research was to explore the influence of delayed cooling treatments on key quality attributes of tomatoes such as fresh weight, total soluble solids (TSS), firmness, and skin color (L*, a*, and a*/b*). Three treatments were applied to freshly harvested tomatoes: immediate storage (IS) after harvest, delayed cooling (DS) leaving tomatoes without cover for one day, and under cover (DSC) separately in a greenhouse and then storage in a controlled chamber at a temperature of 10 ± 1 ℃ and relative humidity of 90 ± 3%. The quality attributes of the stored tomatoes were examined for 15 storage days at 5-day intervals to examine the effects of cooling delay on the postharvest quality of tomatoes stored in a controlled chamber. After 15 days of storage, tomatoes that underwent the three treatments exhibited a progressive weight loss of 1.41%, 1.86%, and 1.69% for the IS, DS, and DSC treatments, respectively. Slower firmness reduction (31.2%) was observed for tomatoes with the IS treatment than for those with the other treatments over the storage duration. During the storage time, tomatoes that experienced the DS treatment exhibited higher increases in TSS (°Bx) values (4.79 to 5.76) than other tomato samples. Smaller changes in color values (L*, a*, and a*/b*) were observed for IS-treated tomatoes. During the storage time, overall changes were slower for IS-treated tomato samples than for those with other treatments. The results of this research indicate that the accumulation of field heat due to cooling delay could decrease the overall quality attributes of tomatoes in the market chain. The immediate transfer of harvested tomatoes to a cool temperature is strongly recommended.

    Citation: Md. Shaha Nur Kabir, Kamal Rasool, Wang-Hee Lee, Seong-In Cho, Sun-Ok Chung. Influence of delayed cooling on the quality of tomatoes (Solanum lycopersicum L.) stored in a controlled chamber[J]. AIMS Agriculture and Food, 2020, 5(2): 272-285. doi: 10.3934/agrfood.2020.2.272

    Related Papers:

    [1] Wenxue Huang, Yuanyi Pan . On Balancing between Optimal and Proportional categorical predictions. Big Data and Information Analytics, 2016, 1(1): 129-137. doi: 10.3934/bdia.2016.1.129
    [2] Dongyang Yang, Wei Xu . Statistical modeling on human microbiome sequencing data. Big Data and Information Analytics, 2019, 4(1): 1-12. doi: 10.3934/bdia.2019001
    [3] Wenxue Huang, Xiaofeng Li, Yuanyi Pan . Increase statistical reliability without losing predictive power by merging classes and adding variables. Big Data and Information Analytics, 2016, 1(4): 341-348. doi: 10.3934/bdia.2016014
    [4] Jianguo Dai, Wenxue Huang, Yuanyi Pan . A category-based probabilistic approach to feature selection. Big Data and Information Analytics, 2018, 3(1): 14-21. doi: 10.3934/bdia.2017020
    [5] Amanda Working, Mohammed Alqawba, Norou Diawara, Ling Li . TIME DEPENDENT ATTRIBUTE-LEVEL BEST WORST DISCRETE CHOICE MODELLING. Big Data and Information Analytics, 2018, 3(1): 55-72. doi: 10.3934/bdia.2018010
    [6] Xiaoxiao Yuan, Jing Liu, Xingxing Hao . A moving block sequence-based evolutionary algorithm for resource investment project scheduling problems. Big Data and Information Analytics, 2017, 2(1): 39-58. doi: 10.3934/bdia.2017007
    [7] Yaguang Huangfu, Guanqing Liang, Jiannong Cao . MatrixMap: Programming abstraction and implementation of matrix computation for big data analytics. Big Data and Information Analytics, 2016, 1(4): 349-376. doi: 10.3934/bdia.2016015
    [8] Tao Wu, Yu Lei, Jiao Shi, Maoguo Gong . An evolutionary multiobjective method for low-rank and sparse matrix decomposition. Big Data and Information Analytics, 2017, 2(1): 23-37. doi: 10.3934/bdia.2017006
    [9] Wenxue Huang, Qitian Qiu . Forward Supervised Discretization for Multivariate with Categorical Responses. Big Data and Information Analytics, 2016, 1(2): 217-225. doi: 10.3934/bdia.2016005
    [10] Yiwen Tao, Zhenqiang Zhang, Bengbeng Wang, Jingli Ren . Motality prediction of ICU rheumatic heart disease with imbalanced data based on machine learning. Big Data and Information Analytics, 2024, 8(0): 43-64. doi: 10.3934/bdia.2024003
  • Quality degradation due to inappropriate postharvest handling, including short exposure to high and variable temperature environments and cooling delay, is a critical issue for maintaining the freshness of vegetables and fruits in further marketing chains and final consumption. The goal of this research was to explore the influence of delayed cooling treatments on key quality attributes of tomatoes such as fresh weight, total soluble solids (TSS), firmness, and skin color (L*, a*, and a*/b*). Three treatments were applied to freshly harvested tomatoes: immediate storage (IS) after harvest, delayed cooling (DS) leaving tomatoes without cover for one day, and under cover (DSC) separately in a greenhouse and then storage in a controlled chamber at a temperature of 10 ± 1 ℃ and relative humidity of 90 ± 3%. The quality attributes of the stored tomatoes were examined for 15 storage days at 5-day intervals to examine the effects of cooling delay on the postharvest quality of tomatoes stored in a controlled chamber. After 15 days of storage, tomatoes that underwent the three treatments exhibited a progressive weight loss of 1.41%, 1.86%, and 1.69% for the IS, DS, and DSC treatments, respectively. Slower firmness reduction (31.2%) was observed for tomatoes with the IS treatment than for those with the other treatments over the storage duration. During the storage time, tomatoes that experienced the DS treatment exhibited higher increases in TSS (°Bx) values (4.79 to 5.76) than other tomato samples. Smaller changes in color values (L*, a*, and a*/b*) were observed for IS-treated tomatoes. During the storage time, overall changes were slower for IS-treated tomato samples than for those with other treatments. The results of this research indicate that the accumulation of field heat due to cooling delay could decrease the overall quality attributes of tomatoes in the market chain. The immediate transfer of harvested tomatoes to a cool temperature is strongly recommended.


    1. Introduction

    Multi-nominal data are common in scientific and engineering research such as biomedical research, customer behavior analysis, network analysis, search engine marketing optimization, web mining etc. When the response variable has more than two levels, the principle of mode-based or distribution-based proportional prediction can be used to construct nonparametric nominal association measure. For example, Goodman and Kruskal [3,4] and others proposed some local-to-global association measures towards optimal predictions. Both Monte Carlo and discrete Markov chain methods are conceptually based on the proportional associations. The association matrix, association vector and association measure were proposed by the thought of proportional associations in [9]. If there is no ordering to the response variable's categories, or the ordering is not of interest, they will be regarded as nominal in the proportional prediction model and the other association statistics.

    But in reality, different categories in the same response variable often are of different values, sometimes much different. When selecting a model or selecting explanatory variables, we want to choose the ones that can enhance the total revenue, not just the accuracy rate. Similarly, when the explanatory variables with cost weight vector, they should be considered in the model too. The association measure in [9], ωY|X, doesn't consider the revenue weight vector in the response variable, nor the cost weight in the explanatory variables, which may lead to less profit in total. Thus certain adjustments must be made for a better decisionning.

    To implement the previous adjustments, we need the following assumptions:

    X and Y are both multi-categorical variables where X is the explanatory variable with domain {1,2,...,α} and Y is the response variable with domain {1,2,...,β} respectively;

    the amount of data collected in this article is large enough to represent the real distribution;

    the model in the article mainly is based on the proportional prediction;

    the relationship between X and Y is asymmetric;

    It needs to be addressed that the second assumption is probably not always the case. The law of large number suggests that the larger the sample size is, the closer the expected value of a distribution is to the real value. The study of this subject has been conducted for hundreds of years including how large the sample size is enough to simulate the real distribution. Yet it is not the major subject of this article. The purpose of this assumption is nothing but a simplification to a more complicated discussion.

    The article is organized as follows. Section 2 discusses the adjustment to the association measure when the response variable has a revenue weight; section 3 considers the case where both the explanatory and the response variable have weights; how the adjusted measure changes the existing feature selection framework is presented in section 4. Conclusion and future works will be briefly discussed in the last section.


    2. Response variable with revenue weight vector

    Let's first recall the association matrix {γs,t(Y|X)} and the association measure ωY|X and τY|X.

    γs,t(Y|X)=E(p(Y=s|X)p(Y=t|X))p(Y=s)=αi=1p(X=i|Y=s)p(Y=t|X=i);s,t=1,2,..,βτY|X=ωY|XEp(Y)1Ep(Y)ωY|X=EX(EY(p(Y|X)))=βs=1αi=1p(Y=s|X=i)2p(X=i)=βs=1γssp(Y=s) (1)

    γst(Y|X) is the (s,t)-entry of the association matrix γ(Y|X) representing the probability of assigning or predicting Y=t while the true value is in fact Y=s. Given a representative train set, the diagonal entries, γss, are the expected accuracy rates while the off-diagonal entries of each row are the expected first type error rates. ωY|X is the association measure from the explanatory variable X to the response variable Y without a standardization. Further discussions to these metrics can be found in [9].

    Our discussion begins with only one response variable with revenue weight and one explanatory variable without cost weight. Let R=(r1,r2,...,rβ) to be the revenue weight vector where rs is the possible revenue for Y=s. A model with highest revenue in total is then the ideal solution in reality, not just a model with highest accuracy. Therefore comes the extended form of ωY|X with weight in Y as in 2:

    Definition 2.1.

    ˆωY|X=βs=1αi=1p(Y=s|X=i)2rsp(X=i)=βs=1γssp(Y=s)rsrs>0,s=1,2,3...,β (2)

    Please note that ωY|X is equivalent to τY|X for given X and Y in a given data set. Thus the statistics of τY|X will not be discussed in this article.

    It is easy to see that ˆωY|X is the expected total revenue for correctly predicting Y. Therefore one explanatory variable X1 with ˆωY|X1 is preferred than another X2 if ˆωY|X1ˆωY|X2. It is worth mentioning that ˆωY|X is asymmetric, i.e., ˆωY|XˆωX|Y and that ωY|X=ˆωY|X if r1=r2=...=rβ=1.

    Example.Consider a simulated data motivated by a real situation. Suppose that variable Y is the response variable indicating the different computer brands that the customers bought; X1, as one explanatory variable, shows the customers' career and X2, as another explanatory variable, tells the customers' age group. We want to find a better explanatory variable to generate higher revenue by correctly predicting the purchased computer's brand. We further assume that X1 and X2 both contain 5 categories, Y has 4 brands and the total number of rows is 9150. The contingency table is presented in 1.

    Table 1. Contingency tables:X1 vs Y and X2 vs Y.
    X1|Y y1 y2 y3 y4 X2|Y y1 y2 y3 y4
    x11 1000 100 500 400 x21 500 300 200 1500
    x12 200 1500 500 300 x22 500 400 400 50
    x13 400 50 500 500 x23 500 500 300 700
    x14 300 700 500 400 x24 500 400 1000 100
    x15 200 500 400 200 x25 200 400 500 200
     | Show Table
    DownLoad: CSV

    Let us first consider the association matrix {γY|X}. Predicting Y with the information of X1, or X2 is given by the association matrix γ(Y|X1), or γ(Y|X2) as in Table 2.

    Table 2. Association matrices:X1 vs Y and X2 vs Y.
    Y|ˆY ^y1|X1 ^y2|X1 ^y3|X1 ^y4|X1 Y|ˆY ^y1|X2 ^y2|X2 ^y3|X2 ^y4X2
    y1 0.34 0.18 0.27 0.22 y1 0.26 0.22 0.27 0.25
    y2 0.13 0.48 0.24 0.15 y2 0.25 0.24 0.29 0.23
    y3 0.24 0.28 0.27 0.21 y3 0.25 0.24 0.36 0.15
    y4 0.25 0.25 0.28 0.22 y4 0.22 0.18 0.14 0.46
     | Show Table
    DownLoad: CSV

    Please note that Y contains the true values while ˆY is the guessed one. One can see from this table that the accuracy rate of predicting y1 and y2 by X1 on the left are larger than that on the right. The cases of y3 and y4, on the other hand, are opposite.

    The correct prediction contingency tables of X1 and Y, denoted as W1, plus that of X2 and Y, denoted as W2, can be simulated through Monte Carlo simulation as in Table 3.

    Table 3. Contingency table for correct predictions: W1 and W2.
    X1|Y y1 y2 y3 y4 X2|Y y1 y2 y3 y4
    x11 471 6 121 83 x21 98 34 19 926
    x12 101 746 159 107 x22 177 114 113 1
    x13 130 1 167 157 x23 114 124 42 256
    x14 44 243 145 85 x24 109 81 489 6
    x15 21 210 114 32 x25 36 119 206 28
     | Show Table
    DownLoad: CSV

    The total number of the correct predictions by X1 is 3142 while it is 3092 by X2, meaning the model with X1 is better than X2 in terms of accurate prediction. But it maybe not the case if each target class has different revenues. Assuming the revenue weight vector of Y is R=(1,1,2,2), we have the association measure of ωY|X, and ˆωY|X as in Table 4:

    Table 4. Association measures: ωY|X, and ˆωY|X.
    X ωY|X ˆωY|X total revenue average revenue
    X1 0.3406 0.456 4313 0.4714
    X2 0.3391 0.564 5178 0.5659
     | Show Table
    DownLoad: CSV

    Given that revenue=i,sWi,skrs,i=1,2,...,α,s=1,2,...,β,k=1,2, we have the revenue for W1 as 4313, and that for W2 as 5178. Divide the revenue by the total sample size, 9150, we can obtain 0.4714 and 0.5659 respectively. Contrasting these to ˆωY|X1 and ˆωY|X2 above, we believe that they are similar, which means then ˆωY|X is truly the expected revenue.

    In summary, it is possible for an explanatory variable X with bigger ˆωY|X, i.e, the larger revenue, but with smaller ωY|X, i.e., the smaller association. When the total revenue is of the interest, it should be the better variable to be selected, not the one with larger association.


    3. Explanatory variable with cost weight and response variable with revenue weight

    Let us further discuss the case with cost weight vector in predictors in addition to the revenue weight vector in the dependent variable. The goal is to find a predictor with bigger profit in total. We hence define the new association measure as in 3.

    Definition 3.1.

    ˉωY|X=αi=1βs=1p(Y=s|X=i)2rscip(X=i) (3)

    ci>0,i=1,2,3,...,α, and rs>0,s=1,2,...,β.

    ci indicates the cost weight of the ith category in the predictor and rs means the same as in the previous section. ˉωY|X is then the expected ratio of revenue and cost, namely RoI. Thus a larger ˉωY|X means a bigger profit in total. A better variable to be selected then is the one with bigger ˉωY|X. We can see that ˉωY|X is an asymmetric measure, meaning ˉωY|XˉωY|X. When c1=c2=...=cα=1, Equation 3 is exactly Equation 2; when c1=c2=...=cα=1 and r1=r2=...=rβ=1, equation 3 becomes the original equation 1.

    Example. We first continue the example in the previous section with new cost weight vectors for X1 and X2 respectively. Assuming C1=(0.5,0.4,0.3,0.2,0.1), C2=(0.1,0.2,0.3,0.4,0.5) and R=(1,1,1,1), we have the associations in Table 5.

    Table 5. Association with/without cost vectors: X1 and X2.
    X ωY|X ˆωY|X ˉωY|X total profit average profit
    X1 0.3406 0.3406 1.3057 12016.17 1.3132
    X2 0.3391 0.3391 1.8546 17072.17 1.8658
     | Show Table
    DownLoad: CSV

    By profit=i,sWi,skrsCki,i=1,2,..,α;s=1,2,..,β and k=1,2 where Wk is the corresponding prediction contingency table, we have the profit for X1 as 12016.17 and that of X2 as 17072.17. When both divided by the total sample size 9150, they change to 1.3132 and 1.8658, similar to ˉω(Y|X1) and ˉω(Y|X2). It indicates that ˉωY|X is the expected RoI. In this example, X2 is the better variable given the cost and the revenue vectors are of interest.

    We then investigate how the change of cost weight affect the result. Suppose the new weight vectors are: R=(1,1,1,1), C1=(0.1,0.2,0.3,0.4,0.5) and C2=(0.5,0.4,0.3,0.2,0.1), we have the new associations in Table 6.

    Table 6. Association with/without new cost vectors: X1 and X2.
    X ωY|X ˆωY|X ˉωY|X total profit average profit
    X1 0.3406 0.3406 1.7420 15938.17 1.7419
    X2 0.3391 0.3391 1.3424 12268.17 1.3408
     | Show Table
    DownLoad: CSV

    Hence ˉωY|X1>ˉωY|X2, on the contrary to the example with the old weight vectors. Thus the right amount of weight is critical to define the better variable regarding the profit in total.


    4. The impact on feature selection

    By the updated association defined in the previous section, we present the feature selection result in this section to a given data set S with explanatory categorical variables V1,V2,..,Vn and a response variable Y. The feature selection steps can be found in [9].

    At first, consider a synthetic data set simulating the contribution factors to the sales of certain commodity. In general, lots of factors could contribute differently to the commodity sales: age, career, time, income, personal preference, credit, etc. Each factor could have different cost vectors, each class in a variable could have different cost as well. For example, collecting income information might be more difficult than to know the customer's career; determining a dinner waitress' purchase preference is easier than that of a high income lawyer. Therefore we just assume that there are four potential predictors, V1,V2,V3,V4 within the data set with a sample size of 10000 and get a feature selection result by monte carlo simulation in Table 7.

    Table 7. Simulated feature selection: one variable.
    X |Dmn(X)| ωY|X ˉωY|X total profit average profit
    V1 7 0.3906 3.5381 35390 3.5390
    V2 4 0.3882 3.8433 38771 3.8771
    V3 4 0.3250 4.8986 48678 4.8678
    V4 8 0.3274 3.7050 36889 3.6889
     | Show Table
    DownLoad: CSV

    The first variable to be selected is V1 using ωY|X as the criteria according to [9]. But it is V3 that needs to be selected as previously discussed if the total profit is of interest. Further we assume that the two variable combinations satisfy the numbers in Table 8 by, again, monte carlo simulation.

    Table 8. Simulated feature selection: two variables.
    X1,X2 |Dmn(X1,X2)| ωY|(X1,X2) ˉωY|(X1,X2) total profit average profit
    V1,V2 28 0.4367 1.8682 18971 1.8971
    V1,V3 28 0.4025 2.1106 20746 2.0746
    V1,V4 56 0.4055 1.8055 17915 1.7915
    V3,V2 16 0.4055 2.3585 24404 2.4404
    V3,V4 32 0.3385 2.0145 19903 1.9903
     | Show Table
    DownLoad: CSV

    As we can see, all ωY|(X1,X2)ωY|X1, but it is not case for ˉωY|(X1,X2) since the cost gets larger with two variables thus the profit drops down. As in one variable scenario, the better two variable combination with respect to ωY|(X1,X2) is (V1,V2) while ˉωY|(X1,X2) suggests (V3, V2) is the better choice.

    In summary, the updated association with cost and revenue vector not only changes the feature selection result by different profit expectations, it also reflects a practical reality that collecting information for more variables costs more thus reduces the overall profit, meaning more variables is not necessarily better on a Return-Over-Invest basis.


    5. Conclusions and remarks

    We propose a new metrics, ¯ωY|X in this article to improve the proportional prediction based association measure, ωY|X, to analyze the cost and revenue factors in the categorical data. It provides a description to the global-to-global association with practical RoI concerns, especially in a case where response variables are multi-categorical.

    The presented framework can also be applied to high dimensional cases as in national survey, misclassification costs, association matrix and association vector [9]. It should be more helpful to identify the predictors' quality with various response variables.

    Given the distinct character of this new statistics, we believe it brings us more opportunities to further studies of finding the better decision for categorical data. We are currently investigating the asymptotic properties of the proposed measures and it also can be extended to symmetrical situation. Of course, the synthetical nature of the experiments in this article brings also the question of how it affects a real data set/application. It is also arguable that the improvements introduced by the new measures probably come from the randomness. Thus we can use k-fold cross-validation method to better support our argument in the future.




    [1] Wilcox JK, Catignani GL, Lazarus S, et al. (2003) Tomatoes and cardiovascular health. Crit Rev Food Sci Nutr 43: 1-18. doi: 10.1080/10408690390826437
    [2] Ali A, Maqbool M, Ramachandran S, Alderson PG, et al. (2010) Gum Arabic as a novel edible coating for enhancing shelf life and improving postharvest quality of tomato (Solanum lycopersicum L.) fruit. Postharvest Biol Technol 58: 42-47. doi: 10.1016/j.postharvbio.2010.05.005
    [3] Arab L, Steck S (2000) Lycopene and cardiovascular disease. Am J Clin Nutr 71: 1691-1695. doi: 10.1093/ajcn/71.6.1691S
    [4] Ali A, Magbool M, Alderson PG, Zahid N, et al. (2013) Effect of gum Arabic as an edible coating on antioxidant capacity of tomato (Solanum lycopersicum L.) fruit during storage. Postharvest Biol Technol 76: 119-124.
    [5] Thompson AK (2015) Fruit and vegetables: harvesting, handling and storage, 3 Eds., West Sussex: John Wiley & Sons, Ltd.
    [6] Arah IK, Ahorbo GK, Anku EK, Kumah EK, Amaglo H, et al. (2016) Postharvest handling practices and treatment methods for tomato handlers in developing countries: A mini review. Adv Agric 2016: 1-8.
    [7] Lim BS, Lee JS, Park HJ, Oh SY, Chun JP, et al. (2016) Effects of ethylene treatment on postharvest quality in kiwi fruit. Korean j Agric Sci 43: 340-345. doi: 10.7744/kjoas.20160035
    [8] Ben-Arie R, Lurie S (1986) Prolongation of fruit life after harvest. In: Monselise SP, Hand Book of Fruit Set and Development, Florida: CRC press, 493-520.
    [9] Tolesa GN, Workneh TS (2017) Influence of storage environment, maturity stage and pre-storage disinfection treatments on tomato fruit quality during winter in KwaZulu-Natal, South Africa. J Food Sci Technol 54: 3230-3242. doi: 10.1007/s13197-017-2766-6
    [10] Tano K, Oulé MK, Doyon G, Lencki RW, Arul J, et al. (2007) Comparative evaluation of the effect of storage temperature fluctuation on modified atmosphere packages of selected fruit and vegetables. Postharvest Biol Technol 46: 212-221. doi: 10.1016/j.postharvbio.2007.05.008
    [11] Aghdam MS, Jannatizadeh A, Luo Z, Paliyath G, et al. (2018) Ensuring sufficient intracellular ATP supplying and friendly extracellular ATP signaling attenuates stresses, delays senescence and maintains quality in horticultural crops during postharvest life. Trends Food Sci. Technol 76: 67-81. doi: 10.1016/j.tifs.2018.04.003
    [12] Roberts KP, Sargent SA, Fox AJ, et al. (2002) Effect of storage temperature on ripening and postharvest quality of grape and mini-pear tomatoes. Proceedings of the Florida State Horticultural Society, 115: 80-84.
    [13] Gharezi M, Joshi N, Sadeghian E, et al. (2012) Effect of postharvest treatment on stored cherry tomatoes. J Nutr Food Sci 2: 157.
    [14] Cantwell M (2001) Properties and recommended conditions for the long-term storage of fresh fruits and vegetables, Storage Recommendations. Davis: Department of Plant Sciences, University of California.
    [15] Suslow TV, Cantwell M (2000) Tomato: Recommendations for maintaining postharvest quality. Tomato Produce Facts, Postharvest Technology Center, Davis: University of California.
    [16] Arah IK, Amaglo H, Kumah EK, Ofori H, et al. (2015) Preharvest and postharvest factors affecting the quality and shelf life of harvested tomatoes: A mini review. Int J Agron 2015: 1-6.
    [17] Pila N, Gol NB, Rao TVR, et al. (2010) Effect of post-harvest treatments on physicochemical characteristics and shelf life of tomato (Lycopersicon esculentum Mill.) fruits during storage. American-Eurasian J Agric and Environ Sci 9: 470-479.
    [18] Kusumaningrum D, Lee SH, Lee WH, Mo C, Cho BK, et al. (2015) A review of technologies to prolong the shelf life of fresh tropical fruits in Southeast Asia. J of Biosystems Eng 40: 345-358. doi: 10.5307/JBE.2015.40.4.345
    [19] Kader AA (2005) Increasing food availability by reducing postharvest losses of fresh produce. Acta Horticulture 682: 2169-2175.
    [20] Wu CT (2010) An overview of postharvest biology and technology of fruits and vegetables. In: Huang CC, Proc. of the AARDO Workshop on Technology on Reducing Post-Harvest Losses and Maintaining Quality of Fruit and Vegetables 2010. Taiwan: Agricultural Research Institute, 2-11.
    [21] Kader AA, Stevens MA, Albright-Holton M, Morris LL, Algazi M, et al. (1977) Effect of fruit ripeness when picked on flavor and composition in fresh market tomatoes. J Am Soc Hortic Sci 102: 724-731.
    [22] Satyan SH, Patwardhan MV (1983) Organic acid metabolism during ripening of fruits. Indian J Biochem Biophys 20: 311-314.
    [23] Barrett DM, Beaulieu JC, Shewfelt R, et al. (2010) Color, flavor, texture, and nutritional quality of fresh-cut fruits and vegetables: desirable levels, instrumental and sensory measurement, and the effects of processing. Crit Rev Food Sci Nutr 50: 369-389. doi: 10.1080/10408391003626322
    [24] Simson SP, Straus MC (2010) Post-harvest technology of horticultural crops. Jaipur: Oxford Book Company, 249-302.
    [25] Kim DG, Cho BK, Lee WH, et al. (2016) A novel approach in analyzing agriculture and food systems: Review of modeling and its applications. Korean j Agric Sci 43: 163-175. doi: 10.7744/kjoas.20160019
    [26] Nirupama P, Gol NB, Rao TVR, et al. (2010) Effect of postharvest treatments on physicochemical characteristics and storage life of tomato (Lycopersicon esculentum Mill.) fruits during storage. Am Eurasian J Agric Environ Sci 9: 470-479.
    [27] Gormley RS, Egan S (1978) Firmness and colour of the fruit of some tomato cultivars from various sources during storage. J Sci Food Agric 29: 534-538. doi: 10.1002/jsfa.2740290607
    [28] Choi IL, Yoo TJ, Jung HJ, Kim IS, Kang HM, Lee YB, et al. (2011) Effects of active modified atmosphere packaging on the storability of fresh-cut Paprika. J Bio-Environ Control 20: 227-232.
    [29] Khairi AN, Falah MAF, Suyantohadi A, Takahashi N, Nishina H, et al. (2015) Effect of storage temperatures on color of tomato fruit (Solanum lycopersicum Mill.) cultivated under moderate water stress treatment. Agric Agric Sci Procedia 3: 178-183.
    [30] Pinheiro J, Alegria C, Abreu M, Gonçalves EM, Silva CLM, et al. (2013) Kinetics of changes in the physical quality parameters of fresh tomato fruits (Solanum lycopersicum, cv. 'Zinac') during storage. J Food Eng 114: 338-345.
    [31] Žnidarčič D, Ban D, Oplanić M, Karić L, Požrl T, et al. (2010) Influence of postharvest temperatures on physicochemical quality of tomatoes (Lycopersicon esculentum Mill.). J Food Agric Environ 8: 21-25.
    [32] Guillén F, Castillo S, Zapata PJ, Martínez-Romero D, Serrano M, Valero D, et al. (2007) Efficacy of 1-MCP treatment in tomato fruit. 1. Duration and concentration of 1-MCP treatment to gain an effective delay of postharvest ripening. Postharvest Biol Technol 43: 23-27.
    [33] Moneruzzaman KM, Hossain ABMS, Sani W, Saifuddin M, Alenazi M, et al. (2009) Effect of harvesting and storage conditions on the post-harvest quality of tomato (Lycopersicon esculentum Mill) cv. Roma VF. Aust J Crop Sci 3: 113-121.
    [34] Li L, Lichter A, Kenigsbuch D, Porat R, et al. (2015) Effects of cooling delays at the wholesale market on the quality of fruit and vegetables after retail marketing. J Food Process Pres 39: 2533-2547. doi: 10.1111/jfpp.12504
    [35] Madani B, Mirshekari A, Imahori Y, et al. (2019) Physiological responses to stress. In: Yahia EM, Carrillo-López A, Postharvest Physiology and Biochemistry of Fruits and Vegetables, Massachusetts: Woodhead Publishing, 405-423.
    [36] Batu A (2004) Determination of acceptable firmness and colour values of tomatoes. J Food Eng 61: 471-475. doi: 10.1016/S0260-8774(03)00141-9
    [37] Lana MM, Tijskens LMM, Kooten O, et al. (2005) Effects of storage temperature and fruit ripening on firmness of fresh cut tomatoes. Postharvest Biol Technol 35: 87-95. doi: 10.1016/j.postharvbio.2004.07.001
    [38] Majidi H, Minaei S, Almasi M, Mostofi Y, et al. (2014) Tomato quality in controlled atmosphere storage, modified atmosphere packaging and cold storage. J Food Sci Technol 51: 2155-2161. doi: 10.1007/s13197-012-0721-0
    [39] Mahmood A, Hu Y, Tanny J, Asante EA, et al. (2018) Effects of shading and insect-proof screens on crop microclimate and production: A review of recent advances. Sci Hrotic 241: 241-251. doi: 10.1016/j.scienta.2018.06.078
    [40] Tigist M, Workneh TS, Woldetsadik K, et al. (2013) Effects of variety on the quality of tomato stored under ambient conditions. J Food Sci Technol 50: 477-486. doi: 10.1007/s13197-011-0378-0
    [41] Majidi H, Minaei S, Almasi M, Mostofi Y, et al. (2011) Total soluble solids, titratable acidity and repining index of tomato in various storage conditions. Aust J Basic & Appl Sci 5: 1723-1726.
    [42] Guillén F, Castillo S, Zapata PJ, Martínez-Romero D, Valero D, Serrano M, et al. (2006) Efficacy of 1-MCP treatment in tomato fruit. 2-Effect of cultivar and ripening stage at harvest. Postharvest Biol Technol 42: 235-242.
    [43] Zapata PJ, Guillén F, Martínez-Romero D, Castillo S, Valero D, Serrano M, et al. (2008) Use of alginate or zein as edible coatings to delay postharvest ripening process and to maintain tomato (Solanum lycopersicon Mill.) quality. J Sci Food Agric 88: 1287-1293. doi: 10.1002/jsfa.3220
    [44] Gould WA (1992) Tomato production, processing and technology. Maryland: CTI Publications Inc.
    [45] Luengwilai K, Tananuwong K, Shoemaker CF, Beckles DM, et al. (2010) Starch molecular structure shows little association with fruit physiology and starch metabolism in tomato. J Agric Food Chem 58: 1275-1282. doi: 10.1021/jf9032393
    [46] Gautier H, Lopez-Lauri F, Massot C, Murshed R, Marty I, Grasselly D, Keller C, Sallanon H, Genard M, et al. (2010) Impact of ripening and salinity on tomato fruit ascorbate content and enzymatic activities related to ascorbate recycling. Funct Plant Sci Biotechnol 4: 66-75.
    [47] Beckles DM (2012) Factors affecting the postharvest soluble solids and sugar content of tomato (Solanum lycopersicum L.) fruit: Review. Postharvest Biol Technol 63: 129-140. doi: 10.1016/j.postharvbio.2011.05.016
    [48] Renquist AR, Reid JB (1998) Quality of processing tomato (Lycopersicon esculentum) fruit from four bloom dates in relation to optimal harvest timing. N Z J Crop Hortic Sci 26: 61-168.
    [49] Campbell AD, Huysamer M, Stotz HU, Greve LC, Labavitch JM, et al. (1990) Comparison of ripening processes in intact tomato fruit and excised pericarp discs. Plant Physiol 94: 1582-1589. doi: 10.1104/pp.94.4.1582
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4658) PDF downloads(354) Cited by(8)

Article outline

Figures and Tables

Figures(2)  /  Tables(3)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog