RESEARCH ARTICLE Shelf Life Prediction of ‘Royal Gala

Received: July 21, 2020

Revised: December 1, 2020

Accepted: January 20, 2021

OPEN ACCESS

HORTICULTURAL SCIENCE and TECHNOLOGY

39(3):343-355, 2021

URL: http://www.hst-j.org

pISSN : 1226-8763

eISSN : 2465-8588

This is an Open Access article distributed

under the terms of the Creative Commons

Attribution Non-Commercial License which

permits unrestricted non-commercial use,

distribution, and reproduction in any medium,

provided the original work is properly cited.

Copyrightⓒ2021 Korean Society for

Horticultural Science.

This research was funded by the Ministry of Agriculture's Modern Apple Industry Technology System Project (CARS-28) and Major Scientific and Technological Projects of Shaanxi Province (2020zdzx03-05-01).

Horticultural Science and Technology 343

RESEARCH ARTICLE https://doi.org/10.7235/HORT.20210031

Shelf Life Prediction of ‘Royal Gala’ Apples Based on Quality Attributes and Storage Temperature

Mengke Cao1, Dong Wang

2, Lingyu Qiu

1, Xiaolin Ren

2, and Huiling Ma

1*

1College of Life Science, Northwest A&F University, Yangling, Shanxi 712100, China

2College of Horticulture, Northwest A&F University, Yangling, Shanxi 712100, China

*Corresponding author: [email protected]

Abstract

Phenotypic changes caused by postharvest deterioration of the quality attributes of apples cause

substantial economic losses. Thus, strategies for accurate prediction of the shelf life of apples is

urgently needed. In each of the three consecutive years from 2016 to 2018, freshly harvested ‘Royal

Gala’ apples were stored at 0, 5, 15, and 25°C, respectively. Subsequently, 11 quality attributes

were measured at periodic intervals until the end of storage. To screen fewer and more useful

indexes, three input datasets were considered: temperature, color value (L*, a*, b*, △E, and C*),

weight loss, firmness, titratable acidity, soluble solids content, starch, and reducing ascorbic acid

(D1). The key quality attributes were screened by sparse principal component analysis (SPCA)

(D2) and correlation analysis (CA) (D3), using shelf life as the output layer of the artificial neural

network based on the back propagation (BP ANN) model. The results showed that the correlation

coefficients (r) of the predicted and measured shelf life for D1, D2, and D3 were 0.996, 0.997, and

0.993, respectively, while the mean relative errors were 0.071, 0.074, and 0.074, respectively.

Meanwhile, the relative percent root mean square (RMS) values were 0.088, 0.092, and 0.112,

respectively. The application of SPCA reduced the quality attributes for the input dataset from 12 to

6. Therefore, SPCA-BP ANN was shown to be a useful model for accurate prediction of the

postharvest shelf life of ‘Royal Gala’ apples.

Additional key words: artificial neural network, correlation analysis, model, sparse principal

component analysis, storage

Introduction

China is the world’s largest producer of apples (Malus domestica Borkh), contributing nearly 50%

of the annual global yield (Li et al., 2020). Proper storage mechanisms that ensure a year-round

supply of apples would fundamentally enrich the market and expand the sale volume of the apple

industry. However, efficient apple storage challenges many aspects of quality, including color,

freshness, aroma, flavor, firmness (texture), and the content of beneficial health substances (Veberic

et al., 2010; Ganai et al., 2015; Thewes et al., 2017; Vicent et al., 2018). This deterioration in quality

makes the apples less appealing to consumers, resulting in substantial postharvest economic losses

(Li et al., 2015).

344 Horticultural Science and Technology


Previous research used the changes of volatiles in apple fruits during storage to establish models to predict shelf life and

make satisfactory conclusions. However, determining the types and levels of volatile organic compounds requires

expensive and heavy equipment, making the procedure costly and not scalable (Saevels et al., 2004). Other studies

focusing on extending the shelf life evaluated the changes that occur in apple firmness, color, weight, soluble solids

content (SSC), total sugars, and ascorbic acid content during storage (Shoakhi and Behmadi, 2015; Lee et al., 2017;

Santagata et al., 2018). The above studies reported that the shelf life of apples could be closely related to these indicators,

along with other factors, such as cultivar, storage method, and picking time (Yoo et al., 2016; Weber et al., 2017). This

study used the ‘Royal Gala’ apple cultivar to examine the changing pattern of apple fruit quality and to generate a shelf

life prediction model. To enhance the reliability of our study output, the various parameters of our research data were

recurrently measured for three consecutive years, and the experiment was conducted under four gradient temperatures.

Sparse principal component analysis (SPCA) introduces a penalty function to eliminate some elements in the load

vector, highlights the main part of the principal component, and effectively extracts valid information for determining

quality attributes (Lu and Zhang. 2012; Fu et al., 2017). Currently, SPCA is mainly used to extract key data features from

large-scale data, such as spectral and electrical data (Cai et al., 2013; Gui et al., 2018). Few studies use small-scale data

for SPCA analysis, such as the quality attributes of food. In this study, the performance of SPCA in extracting small-scale

data was tested. Many models can predict shelf life, some of which can only be adopted under specific conditions. For

instance, the gray model is only applicable for an isochronous time series and thus is not suitable for data collected at

different periodic intervals and different temperatures (Yuan and Chen 2016). The Weibull and Arrhenius equations are

temperature limited (Jafari et al., 2017; Yang et al., 2018), meaning that one equation can only predict the shelf life at a

specific temperature. Therefore, none of these models establishes a universal prediction model for different temperatures.

The artificial neural network based on back propagation (BP ANN) is a flexible mathematical structure that identifies

complex nonlinear relationships between input and output datasets (Hsu et al., 1995). BP ANN is currently widely used

in food science research, i.e., firmness prediction in fresh-cut green peppers (Meng et al., 2012), population prediction of

S. typhimurium in surimi (Qin et al., 2017), cultivar identification of various tea germplasms (He et al., 2007), and inosine

monophosphate level prediction in lightly salted silver carp (Shi et al., 2017). ‘Royal Gala’ apple, one of the ‘Gala’ apple

cultivars, ripens during the offseason of apple fruits, which effectively makes up for the gap in the early apple market (Liu

et al., 2002; Fan et al., 2009). Due to the successful application of the BP ANN model on research involving food

materials, the ‘Royal Gala’ apple cultivar was employed as a model material to investigate shelf life capacity.

This research aimed to evaluate changes in the 11 quality attributes of ‘Royal Gala’ apples and thus develop a universal

model for shelf life prediction in ‘Royal Gala’ stored at different temperatures.

Materials and Methods

Preparation and Treatment of Apple

‘Royal Gala’ apples were harvested from a commercial orchard in Shanxi Province of China (108°15'E; 34°23'N) in

mid-August in 2016, 2017, and 2018, respectively. Bagged fruit of uniform size and color and free of disease and injury

were picked, transported back to Northwest A&F University (about a 1-hour trip), and stored at 0°C overnight (about 12



h) to lose field heat. Then, the fruits were randomly separated into four groups and stored at 0, 5, 15, and 25°C under 85%

to 90% relative humidity. Since the variation rate of quality traits was positively correlated with temperature, the sampling

interval is usually arranged consistent with the changes in observation indexes. Therefore, a shorter sampling interval was

arranged for the higher-temperature groups and a longer interval for the lower-temperature group. During storage, 12 fruit

in each group were sampled at periodic intervals of 12 d at 0°C, 9 d at 5°C, 6 d at 15°C, and 3 d at 25°C. The experiment

was conducted for three consecutive years under the same environment conditions, which was regarded as three

independent biological repetitions.

Determination of Fruit Quality Attributes and Shelf Life

A colorimeter (CR-400, Minolta, Tokyo, Japan) was used to measure the color value of ‘Royal Gala’ apples according

to the manufacturer’s instructions. First, whiteboard calibration was done and then the five even points of the fruit's

equator were measured. L* (lightness), a* (red-green), and b* (yellow-blue) values were read directly from colorimeter.

Meanwhile, △E (total color difference) and C* (saturation) values were calculated according to Eqs. (1) and (2),

respectively.

△

(1)

(2)

where L0, a0, and b0 refer to the color values at 0 d.

The fresh weight was measured with a digital scale (JH1200, Jingheng, Beijing, China). The fruit were weighed at 0 d

and on each sampling day during the storage period, and weight loss rate (%) was calculated according to Eq.3.

Weight loss rate (%) = [(W1－W2) / W1] × 100 (3)

where W1 is the weight at 0 d, and W2 is the weight on each sampling day.

Fresh fruit firmness was determined by a penetrometer device (GY-3, Ruiyou, Aldo Brue, Italy) with an 8-mm probe;

for each apple, we sampled three points evenly distributed along the equatorial plane and then peeled off a 1 × 1 cm2 piece

of skin to measure the firmness of the fruit pulp. We recorded the average value of three points, and firmness was

expressed in Newtons, N. The juice from the sample was squeezed out with a juicer (JYZ-D56, Jiuyang, Xi'an, China),

stirred evenly, and then used for the determination of SSC and titratable acidity (TA). The SSC was measured by a

hand-held refractometer (WY032T, Sloon, Tianjin, China) according to the manufacturer’s instructions. TA was

measured by the acid-base titration method (Ye and Jiang, 2007). The anthrone sulfate method (Qi et al., 2015a) was used

to determine the starch content. About 1.5 g of a fresh apple sample was milled with 6 mL of 80% ethanol and then

incubated in a water bath (XMTD-8222, Jinghong, Shanghai, China) at 80°C for 30 min to extract soluble sugar.

Subsequently, the mixture was cooled and centrifuged (TGL-16G, Antingfeige, Shanghai, China) at 2,000 g for 10 min,

and the supernatant was removed (repeated twice). We then mixed the precipitate with 2 mL of deionized water, and it was



gelatinized in a boiling water bath for 15 min. After cooling, the starch was mixed with 2 mL of 9.2 mol·L-1

HClO4 for 15

min of acidolysis and then 4 mL of deionized water was added and centrifuged at 2,000 g for 10 min to collect the

supernatant. Acidolysis of the remaining precipitate was done again with 2 mL of 4.6 mol·L-1

HClO4 for 15 min and then

extracted with 5 mL of deionized water and centrifuged at 2,000 g for 10 min. Subsequently, the supernatants were

combined in a 50-mL volumetric flask and deionized water was added up to the mark. Then, 0.4 mL of the supernatant

was pipetted, mixed with 5 mL anthrone sulfuric acid, and incubated in a boiling water bath for 10 min, and the

absorbance of the mixture was measured at 620 nm (UV-3100, Mapada, Shanghai, China). The starch content was

calculated using Eq. (4). The glucose standard stock solution was used to make a standard curve. Three biological samples

were determined with two technical repeats on each sampling day.

Starch (%) =

(4)

where C is the amount of glucose in the measuring tube obtained from the standard curve (mg), V is the total volume of

the extract (mL), a is the sampling volume during the measurement, and W is the sample weight (mg).

The sensory panel consisted of 10 trained panels to evaluate the overall acceptability of 10 ‘Royal Gala’ apples. The

evaluation indicators and weights were reorganized into 40% freshness appearance, 20% taste crispness, 20% sweet and

sour palatability, 10% flesh quality, and 10% aroma (Cai et al., 2013). Each indicator was scored on a percentile system,

and the weighted average of the five indicators was the score value of each fruit. If the score was less than 60, the fruit is

considered to have no commercial value. The shelf life endpoint was considered as the day when 25–50% of the fruit

became unacceptable by sensory panels (Varela et al., 2005). The shelf life (day) is the day of shelf life endpoint minus

the number of days that the fruit had been stored. The data collected during multiple years of repeated experiments reduce

random errors and improve the representativeness of the quality attribute observations. To make the model more

universal, the average value from 3 years of repeat quality attribute observations was used for subsequent data processing

and analysis.

SPCA

Sparse principal component analysis (SPCA) highlights the main part of each principal component, so the results are

more interpretable. SPCA was conducted in RStudio (3.6.2, free and open-source software) using the SPCA function in

the elasticnet library. The 11 measured quality attributes (color value, L*, a*, b*, △E, C*, weight loss, firmness, titratable

acidity, soluble solids content, starch, and reducing ascorbic acid) and storage temperature were used for the SPCA.

Correlation Analysis (CA)

Correlation analysis (CA) is a powerful tool in multivariate data analysis to find the correlation between two sets of

multidimensional variables; many studies have confirmed the validity of CA (Chu et al., 2013; Salinas et al., 2015; Bian

et al., 2019). In this research, CA was conducted in RStudio (3.6.2, free and open-source software).



Data Preprocessing

All data were preprocessed using Matlab software (2016b, MathWorks Inc., Natick, MA, USA). Since the dimensions

of each indicator differ, parameters with larger values have more impact on the learning of the network, thus slowing the

learning speed of the system or even unable to converge. Data were preprocessed by normalization and anti-normalization

equations, as shown in Eqs. (5) and (6).

Normalization maxmin

min (5)

Anti-normalization ’max－minmin (6)

where min is the smallest value of quality attribute X, max is the biggest value of quality attribute X, is the value of the

quality attribute X at storage time t, y is the normalized value of , and y’ is the anti-normalization value of x.

Structure of BP ANN

The MatLab (2016b, MathWorks Inc., Natick, MA, USA) BP algorithm toolbox was used to build the network using

fruit quality attributes and storage temperature as input and shelf life as the output. The initial weight and threshold were

determined by random methods and then continuously optimized through the learning function and learning rate. The

optimal number of hidden layer neurons was evaluated by determination coefficients (R2), mean relative error, and

relative and percent root mean squares (RMS) of the measured postharvest life and predicted postharvest life. The tansig

function was used as the hidden layer activation function, while purelin was the output layer activation function. The

learning rate was 0.05, learning step was 2000, performance target value was 0.001, the loss function was MSE, and the

MSE function was as shown in Eq. (7). The other parameters were set to default. According to the method described by

Gao (2019), the training dataset:validation dataset ratio was set as 3:1. Thus, 44 observations were separated into 33 for

training and 11 for validation.

MSE =

∑exp (7)

where exp is the experimental value during storage, and pre is the predicted value obtained from the BP ANN.

Performance of BP ANN

Relative percent root mean square (RMS), determination coefficients (R2), and mean relative error were used to

evaluate the performance of BP ANN using Eqs. (8), (9), and (10),

RMS =

∑

expexp

× (8)



A B

C D

E F

Fig. 1. Changes in quality attributes as functions of duration under different temperatures. Data are means from 3 years; *** significant at 0.1% level of significance, ** significant at 5% level of significance, * significant at 1% level of significance.

determination coefficients (R2) =

(9)

mean relative error =

∑

expexp

× (10)

where exp refers to the experimental value obtained during storage, pre is the predict value obtained from BP ANN, SSR

is the regression sum of squares, and SST is the total sum of squares.

Results and Discussion

Variations in the Quality Attributes under the Four Temperatures

As shown in Fig. 1, five quality indicators, including starch, reducing ascorbic acid, firmness, titratable acidity, and

color value L*, reduced with increased storage time. This result suggests that softening and nutrient degradation in the

flesh occurred. The brightness of the skin decreased as the storage time increased, resulting in quality deterioration and



G H

I J

K

Fig. 1. Changes in quality attributes as functions of duration under different temperatures. Data are means from 3 years; *** significant at 0.1% level of significance, ** significant at 5% level of significance, * significant at 1% level of significance (Continued).

ripening of the fruit. These physicochemical changes are consistent with previous research (Joanna et al., 2014; Tran et al.,

2015). The starch and ascorbic acid (VC) levels of apples are reduced because starch undergoes constant decomposition

and conversion into soluble sugars during storage, and VC is easily oxidized under neutral and alkaline conditions

(Wijewardane and Guleria, 2009). As a photosynthetic product, starch accumulates in the fruit and is essential for the

maintenance of cell turgor (Qi et al., 2015b). Loss of fruit firmness during the postharvest storage of apples is mainly due

to the hydrolysis of pectin and cell wall materials in the middle layer, due to the action of cell wall enzymes. Notably, the

firmness loss is associated with ethylene-dependent regulation of an α-L-arabinofuranosidase (Zhang et al., 2010a;

Storch et al., 2015). TA loss is mainly due to the consumption of acids as a respiratory matrix (Zhang et al., 2010b). Five

quality attributes (weight loss, C*, a*, b*, and △E) showed an upward trend with increasing storage time. This result

indicates a change in fruit appearance, i.e., tarnishing and yellowing. During fruit storage, the weight typically reduces

due to water loss and fruit respiration. Fruit water content is an important indicator of fruit quality. Notably, the loss of

huge amounts of water causes fruit shrinkage, which directly affects the appearance, quality, and taste of the fruit. Color

value change is mainly due to the gradual decomposition of chlorophyll and an increase in anthocyanin levels during

postharvest storage (Song et al., 2019). Gradual changes in fresh weight and color inevitably diminished the consumer

acceptability of apples because the quality standards of agricultural products such as apples are primarily determined by

their color (Kus et al., 2017). In summary, with the extension of storage time, the quality of apples declined due to changes

in quality attributes.



Table 1. Partial loading matrix of the SPCA of 11 quality attributes and storage temperature. SPC represents sparse principal component; the loads from SPC6 to SPC12 are all 0

Items SPC1 SPC2 SPC3 SPC4 SPC5

reducing ascorbic acid －0.230 0 0 0 0

starch －0.169 0 0 0 0

titratable acidity 0 0 0 0 0

soluble solids content 0 0 －1 0 0

weight loss 0.484 0 0 0 0

firmness 0 0 0 0 0

L* (lightness) 0 0 0 0 0

a* (redness) 0 0 0 0 0

b* (yellowness) 0 0 0 0 0

△E (total color difference) 0.827 0 0 0 0

C* (chroma) 0 0 0 0 0

Storage temperature 0 -1 0 0 0

SPCA

Table 1 shows the partial loading matrix obtained by SPCA. Loadings of some quality attributes were 0, indicating that

they were negligible in constructing principal components. Other characteristics with non-zero loadings mainly appeared

in the first three sparse principal components (SPCs), and these attributes were mostly physicochemical indicators. The

non-zero loadings of the first sparse principal component (SPC1) included reduced ascorbic acid, starch, weight loss, and

△E (total color difference). The non-zero loading of the second sparse principal component (SPC2) was storage

temperature. The non-zero loading of SPC3 was SSC, and the loadings of each quality attribute in the remaining three

SPCs were 0. The quality attributes with non-zero loadings were the result of dimensionality reduction obtained after

SPCA and could be used as input of BP ANN (D2) to predict the shelf life of ‘Royal Gala’.

Correlation Analysis (CA)

Fig. 2 presents the CA results between the different variables. Starch and firmness were significantly positively

correlated with shelf life (p < 0.001). In contrast, weight loss, color value (a*, b*, △E, and C*), and storage temperature

showed significant negative correlation with shelf life (p < 0.001). Therefore, these quality attributes were regarded as the

result of dimensionality reduction after CA and could be used as an input layer of BP ANN (D3) to predict the shelf life

of ‘Royal Gala’.

BP ANN Model

ANN is a machine learning tool that can simulate complex and non-linear systems due to its ability to use learning

algorithms and establish the relationship between input and output variables (Zobel and Cook, 2011). The correlation

coefficient of the predicted versus measured shelf life is shown in Fig. 3, while the relative error, RMS and mean relative

error of measured versus predicted shelf life are presented in Table 2 and Table 3, respectively. When all measured indicators

(D1) are used as input, the R2, average relative error, and RMS values are 0.996 (p < 0.001), 0.071, and 0.088,



Fig. 2. Correlation analysis between different quality attributes. *** significant at 0.1% level of significance, ** significant at 5% level of significance, * significant at 1% level of significance.

A

B

C

Fig. 3. The correlation between measured and predicted shelf life. Measured represents measured shelf life, Predicted represents predicted shelf life, r represents correlation coefficient, *** significant at 0.1% level of significance, ** significant at 5% level of significance, * significant at 1% level of significance, A, B, and C represent the input layer of BP ANN is D1 temperature, color value (L*, a*, b*, △E, and C*), weight loss, firmness, titratable acidity, soluble solids content, starch, and reducing ascorbic acid); D2 the key quality attributes were screened by sparse principal component analysis (SPCA) and D3 correlation analysis (CA).



Table 2. Validation set results of predicted shelf life and relative error with different input dataset of BP ANN. Measured represents measured shelf life, the unit is day, predicted represents the predicted shelf life, and RE represents the relative error. D1, D2, and D3 represent the input layer of BP ANN is D1. Temperature, color value (L*, a*, b*, △E, and C*), weight loss, firmness, titratable acidity, soluble solids content, starch, and reducing ascorbic acid; D2 the key quality attributes were screened by sparse principal component analysis (SPCA) and D3 correlation analysis (CA)

All indicators (D1) SPCA (D2) Correlation analysis (D3)

Measured Predicted RE Measured Predicted RE Measured Predicted RE

30.000 29.881 0.004 30.000 29.601 0.013 30.000 30.503 0.017

60.000 66.373 0.106 60.000 69.122 0.152 60.000 65.049 0.084

108.000 127.792 0.183 108.000 119.420 0.106 108.000 126.533 0.172

42.000 40.673 0.032 42.000 45.893 0.093 42.000 42.250 0.006

9.000 9.522 0.058 9.000 10.392 0.155 9.000 9.134 0.015

54.000 55.660 0.031 54.000 61.359 0.136 54.000 55.201 0.022

36.000 39.320 0.092 36.000 36.018 0.001 36.000 35.379 0.017

45.000 43.123 0.042 45.000 46.629 0.036 45.000 44.602 0.009

156.000 167.342 0.073 156.000 157.473 0.009 156.000 160.821 0.031

90.000 102.566 0.140 90.000 94.962 0.055 90.000 108.420 0.205

15.000 15.341 0.023 15.000 14.070 0.062 15.000 18.632 0.242

Table 3. Validation set results of mean relative error and RMS with a different input dataset of BP ANN. Measured represents measured shelf life, the unit is day, predicted represents the predicted shelf life, and RE represents the relative error. D1, D2, and D3 represent the input layer of BP ANN is temperature, color value (L*, a*, b*, △E, and C*), weight loss, firmness, titratable acidity, soluble solids content, starch, and reducing ascorbic acid (D1); the key quality attributes were screened by sparse principal component analysis (SPCA) (D2) and correlation analysis (CA) (D3)

All indicators (D1) SPCA (D2) Correlation analysis (D3)

Mean relative error 0.071 0.074 0.074

RMS 0.088 0.092 0.112

respectively. However, when the non-zero indicators obtained from SPCA (D2) are used as the BP ANN input, the R2,

average relative error, and RMS values of measured versus predicted shelf life are 0.997 (p < 0.001), 0.074, and 0.092,

respectively. When the CA indicators (D3) were used as input, the R2, average relative error, and RMS were 0.993 (p <

0.001), 0.074, and 0.112, respectively. The results above show the accuracy of BP ANN in predicting the shelf life of

‘Royal Gala’ apples. The quality attribute identified by the CA was color value, while SPCA identified physical and

chemical indicators. Weight loss, starch, △E, and storage temperature values were obtained by two different

dimensionality reduction methods. Storage temperature is an important environmental variable that affects postharvest

storage time (Abdelfattah et al., 2020). Maintaining fruits at low temperatures is the most common postharvest storage

method for apples. When D1, D2, and D3 were used as the input layers of BP ANN, the number of input layer neurons

were 12, 8, and 6, respectively. Thus, increasing the number of input layer neurons did not cause significant differences

in accuracy. These findings could be attributed to the fact that for a specified dataset, only a few of the selected variables

have the information necessary to support the construction of an effective prediction model (Pan et al., 2016). Accurate

values for all variables were only attained when the six variables obtained by SPCA were used as the BP ANN input layer.

Therefore, SPCA BP ANN is effective for constructing a shelf life prediction model of ‘Royal Gala’ apples, and SPCA is



feasible for use selecting feature variables.

In summary, three datasets, D1, D2, and D3, were used as the BP ANN input layer. We have shown that all three sets

acquired accurate shelf life prediction results. The BP ANN algorithm has been proven powerful in determining the

correlation between the shelf life and quality attributes and storage temperature. Previous research also showed that the

BP ANN model is superior to multiple linear regression in determining the relationship between the input and output

layers (Meng et al., 2012; Li et al., 2014; Chen et al., 2019), mainly due to the complexity of the internal structure of BP

ANN (Zobel and Cook, 2011). CA and SPCA reduced the number of feature quality attributes from 12 to 8 and 6,

respectively, indicating that SPCA is useful for feature selection of small-scale datasets. Furthermore, the combination of

SPCA-BP ANN showed the best performance among the three groups with respect to the lower relative error and RMS.

Weight loss, starch, △E value, reducing ascorbic acid levels, SSC, and the storage temperature obtained from SPCA were

selected as the key indicators for the input layer of BP ANN to predict the shelf life of ‘Royal Gala’ apple. Quality

attributes in the storage environment should be measured as far as possible to avoid the impact of environmental changes

on apples. The potential for using quality attributes to predict shelf life has been researched on several agricultural

products, and models for shelf life prediction have been built mostly based on Weibull (Li et al., 2017) and Arrhenius

equations (Shi et al., 2016). Since the change rate of apple quality attributes shows a complicated trend with increasing

temperature, the accuracy of predicted shelf life using the Arrhenius equation is poor (Shi et al., 2016). Therefore, the

model devised in this study provides a new effective method for estimating the shelf life of ‘Royal Gala’ apples.

Using the ANN algorithm for shelf life prediction of apples is expected to be more rapid and convenient than the

previous methods. The parameters of the BP ANN model constructed by D2 are shown in the supplementary file. We

obtained the shelf life from the corresponding quality attribute values and parameters. The parameters can also be used for

software development to determine postharvest apple shelf life more conveniently and quickly. Accurate shelf life

prediction helps determine the deadline for the marketing of stored apples to avoid postharvest loss, which is a common

concern for consumers, producers, and managers. Successful implementation of this research will provide an alternative

method for shelf life determination of horticultural crops. This method is not limited to the apple cultivar used in this

study. Further, this approach can be applied industrially on fresh crops and other plant materials for shelf life prediction.

Literature Cited

Abdelfattah A, Whitehead S R, Macarisin D, Liu J, Burchard E, Freilich S, Dardick C, Droby S, Wisniewski M (2020) Effect of washing, waxing

and low-temperature storage on the postharvest microbiome of apple. Microorganisms 8:944. doi:10.3390/microorganisms8060944

Bian HX, Tu P, Hua LX (2019) Quality predictions for bruised apples based on dielectric properties. J Food Process Preserv 43:e14006.1-e1

4006.10. doi:10.1111/jfpp.14006

Cai C, Li YC, Ma HL, Li XL (2013) Nondestructive classification of internal quality of applebased on dielectric feature selection. Trans Chin

Soc Agric Eng 29:279-287. doi:10.3969/j.issn.1002-6819.2013.21.035

Chen B (2019) Prediction of PM2.5 Concentration in forest based on BP artificial neural network. J Anhui Agric Sci 614:107-110.

doi:10.19026/ajfst.11.2410

Chu D, Liao LZ, Ng MK (2013) Sparse Canonical Correlation Analysis: New Formulation and Algorithm. IEEE T Pattern Anal 2013,

35:3050-3065. doi:10.1109/TPAMI.2013.104

Fan QZ (2009) The cultivation technique of protected nectarine in Yanchi of Ningxia. Nor Hortic pp 167-168

Fu JH, Zhou XQ, Liu HJ, Lin M (2017) NIRS identification of maturity of huanghua pears with sparse principal component analysis. Phys

Test Chem Anal, Part B: Chem Anal 53:146-151. doi:10.11973/lhjy-hx201702005

Ganai SA, Ahsan H, Wani IA, Lone AA, Mir SA, Wani SM (2015) Colour changes during storage of apple cv. Red delicious- influence of



harvest dates, precooling, calcium chloride and waxing. Int Food Res J 22:196-201. doi:10.1007/BF01744736

Gao Y, Chen B, Liao HM, Lei MG, Li X, Li J, Luo JJ (2019) Recognition method for aggressive behavior of group pigs based on deep

learning. Trans Chin Soc Agric Eng 35:192-200. doi:10.11975/j.issn.1002-6819.2019.23.024

Gui DD, Lu Q, Jin CC, Zhang ZY, Wang HY (2018) Research on raman spectral feature extraction and analytical technology of dairy

products based on sparse principal component analysis. Spectrosc Spectral Anal 38:140-141

He Y, Li XL, Deng XF (2007) Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP

model. J Food Eng 79:1238-1242. doi:10.1016/j.jfoodeng.2006.04.042

Hsu K, Gupta HV, Sorroshian S (1995) Artificial neural network modeling of the rainfall-runoff process. Water Resour Res 31:2517-2530.

doi:10.1029/95WR01955

Jafari SM, Ganje M, Dehnad D, Ghanbari V, Hajitabar J (2017) Arrhenius equation modeling for the shelf life prediction of tomato paste

containing a natural preservative. J Sci Food Agric 97:5216-5222. doi:10.1002/jsfa.8404

Joanna KO, Wojdy OA, Markowski J, Siucińska K (2014) 1-Methylcyclopropene postharvest treatment and their effect on apple quality

during long-term storage time. Eur Food Res Technol 239:603-612. doi:10.1007/s00217-014-2256-2

Kus ZA, Demir B, Eski I, Feyza G, Sezai E (2017) Estimation of the colour properties of apples varieties using neural network; bewertung

der farbmerkmale bei apfelsorten unter verwendung eines neuronalen netzwerkes. Erwerbs-Obstbau 59:291-299. doi:10.1007/s10

341-017-0324-z

Lee J, Jeong MC, Ku KH (2017) Chemical, physical, and sensory properties of 1-MCP-treatedFuji apple (Malus domestica Borkh.) fruits

after long-term cold storage. Appl Biol Chem 60:1-12. doi:10.1007/s13765-017-0288-6

Li C, Ma CJ, Yu J, Zou HY, Peng ZZ (2017) Prediction model for the shelf-life of ponkan fruit wine basedon sensory and physic-chemical

indexes. Food Mach 33:133-138. doi:10.13652/j.issn.1003-5788.2017.07.030

Li MR, Guo JQ, He JQ, Xu CD, Li JK (2020) Mi CR and Tao SL. Possible impact of climate change on apple yield in Northwest China. Theor

Appl Climatol 139:191-203. doi:10.1007/s00704-019-02965-y

Li PB, Yan XR, Xu DR (2014) Comparison of grain yield spatial distribution forecast between the models of BP neural network and

multiple linear regression. J Arid Land Resour and Environ 28:75-79. doi:10.13448/j.cnki.jalre.2014.09.034

Li YJ, Yin MA, Ren XL (2015) Effects of UV-C radiation on storability and quality in harvested apple. Food Sci 14:244-249. doi:10.7506/

spkx1002-6630-201514047

Liu QP, Liu P, Zhao HJ (2002) Biological and photosynthetic physiological characteristics of autotetraploid “Royal Gala” apple. Sci Agric Sin

35:1573-1578

Lu Z, Zhang Y (2012) An augmented Lagrangian approach for sparse principal component analysis. Math Program 135:149-193.

doi:10.1007/s10107-011-0452-4

Meng X, Zhang M, Adhikari B (2012) Prediction of storage quality of fresh‐cut green peppers using artificial neural network. Int J Food

Sci Technol 47:1586-1592. doi:10.1111/j.1365-2621.2012.03007.x

Pan J, Zhuang Y and Fong S (2016) The impact of data normalization on stock market prediction: using SVM and technical indicators.

Commun Comput Info Sci 652:72-88. doi:10.1007/978-981-10-2777-2_7

Qi XD, Wei JM, Li YH (2015a) Carbohydrate metabolism and the key gene expression in apple during fruit texture softening. Acta Hortic

Sinica 42:409-417. doi:10.16420/j.issn.0513-353x.2014-0867

Qi XD, Wei JM, Zhao MW, Peng HL, Zhang HE (2015b) Relationship between fruit softening and the metabolism of sugar and starch and

their related gene expression in postharvest ‘Jingbaili’ fruits. Sci Agric Sin 48:2591-2599. doi:10.3864/j.issn.0578-1752.2015.13.011

Qin T, Liu SW, Mao YZ, Liu X, Tang XZ, Li R, Cai Wj (2017) PSO-based BP-ANN predictive model of S. Typhimurium in processing of surimi

with citric acid. J Food Saf 38:e12420. doi:10.1111/jfs.12420

Saevels S, Lammertyn J, Berna AZ, Veraverbeke EA (2004) An electronic nose and a bass spectrometry-based electronic nose for assessing

apple quality during shelf-life. Postharvest Biol Technol 31:9-19. doi:10.1016/S0925-5214(03)00129-7

Salinas H, Rosa M, González A, Gustavo A, Tiznado H, Martín E (2015) Utilization of physicochemical variables developed from changes

in sensory attributes and consumer acceptability to predict the shelf life of fresh-cut mango fruit. J Food Sci Technol 52:63-77.

doi:10.1007/s13197-013-0992-0

Santagata G, Mallardo S, Fasulo G, Lavermicocca P, Valerio F, Biase MD, Stasio MD, Malinconico M (2018) Pectin-honey coating as novel

dehydrating bioactive agent for cut fruit: enhancement of the functional properties of coated dried fruits. Food Chem 258:104-110.

doi:10.1016/j.foodchem.2018.03.064

Shi C, Cui J, Liu X, Zhang YM, Qin N, Luo Yk (2017) Application of artificial neural network to predict the change of inosine

monophosphate for lightly salted silver carp (hypophthalmichthys molitrix) during thermal treatment and storage. J Food Process

Preserv 41:e13246. doi:10.1111/jfpp.13246

Shi WN, Wang RL, Ma HL (2016) Shelf-life prediction of apple based on quality responses to temperature. Nor Hortic 15:116-123.

doi:10.11937/bfyy.201615029

Shoakhi F, Behmadi H (2015) Investigation on the effects of 1-Methylcyclopropene on the postharvest quality attributes of apple. Food

Sci Technol 13:175-184. doi:10.17660/ActaHortic.2015.1104.47

Song P, Ding YF, Zhuo QM, Li H, Wang TN, Xu ZH, Cai H (2019) Physiological and biochemical characteristics of leaves during the color

change period of three species of euonymus in autumn and winter. Acta Bot Bor-Occid Sin 39:669-676. doi:10.7606/j.issn.1000-40

25.2019.04.0669



Storch TT, Finatto T, Pegoraro C, Cero JD, Laurens F, Rombaldi CV, Quecini V, Girardi CL (2015) Ethylene-dependent regulation of an

alpha-l-arabinofuranosidase is associated to firmness loss in ‘Gala’ apples under long term cold storage. Food Chem. doi:10.1016/j.f

oodchem.2015.02.123

Thewes FR, Brackmann A, Rogerio DOA, Eduarda SB, Erani ES, Roger W (2017) Dynamic controlled atmosphere storage suppresses

metabolism and enhances volatile concentrations of ‘Galaxy’ apple harvested at three maturity stages. Postharvest Biol Technol

127:1-13. doi:10.1016/j.postharvbio.2017.01.002

Tran DT, Verlinden BE, Hertog M, Nicola BM (2015) Monitoring of extremely low oxygen control atmosphere storage of ‘Greenstar’

apples using chlorophyll fluorescence. Sci Hortic 184:18-22. doi:10.1016/j.scienta.2014.11.014

Varela P, Salvador A, Fiszman S (2005) Shelf-life estimation of ‘Fuji’ apples: Sensory characteristics and consumer acceptability.

Postharvest Biol Tec 38:18-24. doi:10.1016/j.postharvbio.2005.05.009

Veberic R, Schmitzer V, Petkovsek MM, Stampar F (2010) Impact of shelf life on content of primary and secondary metabolites in apple

(Malus domestica Borkh.). J Food Sci 75:461-468. doi:10.1111/j.1750-3841.2010.01823.x

Vicent V, Ndoye FT, Verboven P, Nicolai BM, Alvarez G (2018) Quality changes kinetics of apple tissue during frozen storage with

temperature fluctuations. Int J Refrig 92:165-175. doi:10.1016/j.ijrefrig.2018.05.023

Weber A, Thewes FR, Anese RDO, Both V, Pavanello EP, Brackmann A (2017) Dynamic controlled atmosphere (DCA): interaction between

DCA methods and 1-methylcyclopropene on ‘Fuji Suprema’ apple quality. Food Chem 235:136-144. doi:10.1016/j.foodchem.2017.

05.047

Wijewardane RMNA, Guleria SPS (2009) Combined effects of pre-cooling, application of natural extracts and packaging on the storage

quality of apple (Malus domestica Borkh.) cv. Royal Delicious. Tro Agric Res 21:10-20. doi:10.4038/tar.v21i1.2582

Yang XH, Deng LZ, Mujumdar AS, Xiao HW, Zhang Q, Kan Z (2018) Evolution and modeling of colour changes of red pepper (Capsicum

annuum L.) during hot air drying. J Food Eng 231:101-108. doi:10.1016/j.jfoodeng.2018.03.013

Ye Q, Jiang ZB (2007) Determination of vitamin C by acid-base titration and iodine titration. Phys Test Chem Anal, Part B: Chem Anal

43:410-412. doi:10.3321/j.issn:1001-4020.2007.05.026

Yoo J, Lee J, Kwon SI, Chung KH, Dong HL, Choi IM Mattheis J and Kang I (2016) Differences in ethylene and fruit quality attributes during

storage in new apple cultivars. Korean J Hortic Sci Technol 34:257-268. doi:10.12972/kjhst.20160027

Yuan C, Chen D (2016) Effectiveness of the GM (1,1) model on linear growth sequence and its application in global primary energy

consumption prediction. Kybernetes 45:1472-1485. doi:10.1108/K-02-2016-0027

Zhang PL, Chen FS, Yang HS, Li Lt, Gong BW,Wang LL (2010a) Research advances on cell wall disassembly in fruit ripening and softening.

Food Sci Tech 035:62-66. doi:10.13684/j.cnki.spkj.2010.11.059

Zhang HX, Ning JL, Ji H (2010b) Research advances on quality and physiological changes of postharvest fruit. J HeBei Agr Sci 14:54-56.

doi:10.3969/j.issn.1088-1631.2010.02.024

Zobel CW, Cook DF (2011) Evaluation of neural network variable influence measures for process control. Eng Appl Artif Intel

24:803-812. doi:10.1016/j.engappai.2011.03.001

Documents

RESEARCH ARTICLE Shelf Life Prediction of ‘Royal Gala