quality detection of postharvest litchi based on

HORTSCIENCE 55(4):476–482. 2020. https://doi.org/10.21273/HORTSCI14750-19

Quality Detection of Postharvest LitchiBased on Electronic Nose: A FeasibleWay for Litchi Fruit Supervisionduring Circulation ProcessSai XuPublic Monitoring Center for Agro-product of Guangdong Academy ofAgricultural Sciences, Guangzhou 510640, China

Huazhong LuGuangdong Academy of Agricultural Sciences, Guangzhou 510640, China

Xiuxiu SunIndian River Research and Education Center, University of Florida,Ft. Pierce, FL 34845

Additional index words. fruit, intelligent recognition, mechanical injury, storage quality

Abstract. Susceptibility to mechanical injury and fast decay rates are currently two mainproblems of litchi fruit after harvesting. To achieve better postharvest management of litchifruit, this study aimed to find an effective method of litchi fruit supervision during thecirculation process that included mechanical injury detection and storage quality detection.For mechanical injury detection, injury-free litchis without any treatment and litchis withmild and severemechanical injurieswere dropped from80 and110 cmhigh, respectively. Theelectronic nose (E-nose) response, total soluble solid (TSS), and titratable acidity (TA) ofsamples were tested on days 0, 1, 2, 3, 4, and 5 after injury at room temperature. For storagequality detection, normal litchiswere stored in a cold environment. TheE-nose response, TSS,and TA of samples were tested on storage days 0, 3, 6, 10, 15, 19, and 24. The experimentalresults showed that mechanical injury not only accelerated pericarp browning but alsoaccelerated flavor (TA and TSS) loss. The browning index quickly increased during storage,and the TSS and TA of defect-free litchis changed only barely at room temperature andduring cold environment storage. After feature extraction, mechanical injury of litchi can bewell-detected byE-nose fromday 1 to day 4 after injury. The bestmechanical injury detectiontime of litchi fruit is at day 4 after injury under room temperature storage conditions. Aftersingular sensor elimination and comprehensive feature extraction, the storage time andbrowning degree, but not TSS and TA, of litchi fruit can be detected by E-nose. E-nose datapreprocessing should differ according to the litchi variety and detection target.

Litchi (Litchi chinensis) is a subtropical totropical fruit with an attractive red appear-ance, great taste, and rich nutritive value that

has been highly enjoyed by consumers world-wide for many years (Ali et al., 2016; Gonget al., 2014). However, postharvest litchi isvery fragile, which is mainly indicatedby its susceptibility to mechanical injury(Chen et al., 2014) and high decay rate(Zhang and Quantick, 1997). The fragilityof postharvest litchi has been given muchattention by researchers in past decades,but there are still many problems thatrequire further research.

Mechanical injury primarily occurs dur-ing harvest and the transportation process,which has been given less attention. Due tothe thin pericarp, thick flesh, and high watercontent of litchi fruit, cell rupture, cell dis-ruption, and cell separation are easily causedby collision and extrusion (Chen et al., 2013a).Mechanical injury also opens a channel forpathogenic bacteria to enter more easily,which increases the decay rate of fruit. Litchifruit with serious decay due to mechanicalinjury loses its commercial value and shouldbe removed so they do not infect the sur-rounding litchiswith their pathogenic bacteria.The influence of mechanical injury on litchi

pericarp (Chen et al., 2013a, 2013b) has beenanalyzed, but how mechanical injury affectsthe storage quality of litchi is still unknown.Accurate detection of the initial mechanicalinjury to litchi is crucial for postharvest litchimanagement; however, it has not yet beenreported.

In addition to mechanical injury, the nat-ural decay rate of litchi fruit is incredibly fastafter harvesting (Dharini et al., 2008). Thered color of postharvest litchi pericarp rap-idly fades and turns fully brown within a fewdays if stored at room temperature due to thedegradation of anthocyanin in its pericarp(Hu et al., 2004). Although the current pres-ervation technology can slow the decay rateof litchi to some extent (Khan et al., 2012),the reality of the fast decay of postharvestlitchi fruit is still a problem. Fully brown litchihas worse resistance to pathogenic bacteriaand almost zero commercial value. There-fore, a rapid and accurate litchi storage qual-ity detection method should be developed forsellers to handle litchi fruit storage timely andaccurately.

Fresh food with less storage time hasan increasingly important role in consumerhabits because of better standards of living(Elshiekh and Habiba, 1996). Because coldstorage extends the storage life of litchifruit, the outward appearance changes less,especially during the initial stage. Manyconsumers want to know the storage time oflitchi.

At present, there are two traditionalmethodsof litchi quality detection: the sensory de-tection method (Alves et al., 2011) and thephysicochemical detection method (Huanget al., 2016). The sensor detection methodevaluates the qualities of litchi fruit such asthe pericarp color, flavor, and fragrance basedon multiple human perceptive organs. Thephysicochemical detection method detectsthe total soluble solid content, titratable acid-ity, and weight using chemical analysis orphysical measurements. The sensor detec-tion method provides direct evaluation re-sults from humans but is flawed because it istime-consuming, labor-intensive, and easilyaffected by human subjectivity. The physi-ochemical detection method is objective andaccurate, but it is destructive, complicated,and time-consuming. Therefore, the tradi-tional ways cannot meet the requirements ofthe progressing litchi industry. Even thoughmachine vision (Xiong et al., 2011) and spec-trum technologies (Xiong et al., 2018) haveallowed intelligent and fast detection of manyagricultural products, they are unsuitable forstored litchi quality detection because litchifruits cover each other during storage.

The electronic nose (E-nose), also knownas a bionic olfaction instrument, acquiressample information by mimicking the humanolfactory system (R€ock et al., 2008). An E-noseis usually composed of a sampling and cleaningchannel, gas-sensitive sensor array, and pat-tern recognition subsystem. Furthermore, theE-nose has a sensor array that contains sev-eral gas sensors that are sensitive to differ-ent substances, which gives the entire sensor

Received for publication 16 Dec. 2019. Acceptedfor publication 9 Jan. 2020.Published online 21 February 2020.We thank the National Natural Science Foundationof China (31901404), Guangzhou Science andTechnology Planning Program (201904010199),Research and Development Program in Key Areasof Guangdong province (2018B0202240001),New Developing Subject Construction Programof Guangdong Academy of Agricultural Science(Project No. 201802XX), the Presidential Founda-tion of Guangdong Academy of Agricultural Sci-ence (Project No. 201920), and the Special Fund ofGuangdong Academy of Agricultural Science forScientific and Technological Talents Introduction/Cultivation. We also thank the anonymous re-viewers for their critical comments and suggestionsto improve the manuscript.H.L. is the corresponding author. E-mail: [email protected] is an open access article distributed under theCC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/).

476 HORTSCIENCE VOL. 55(4) APRIL 2020

https://doi.org/10.21273/HORTSCI14750-19

mailto:[email protected]

mailto:[email protected]

array the ability to detect simple and complexodors (Pearce et al., 2006). The E-nose is aportable tool that can detect sample qual-ity easily, quickly, and intelligently. Com-pared with sensory and physicochemicaldetection methods, the E-nose can over-come the flaws associated with time andlabor requirements, destruction, complica-tions, and human subjectivity. Compared withthe E-tongue, the E-nose can nondestructivelydetect characteristics of samples (Zhang andTong, 2005). Compared with other machinedetection methods like machine vision andspectrum, the E-nose can overcome the limitof the visual angle. Therefore, the E-nose ismore suitable than other detection methods forlitchi quality supervision.

Accordingly, this study applied an E-noseto detect the quality of litchis with mechan-ical injuries and normal litchis after harvest-ing to determine a feasible method ofexpanding litchi quality supervision duringthe postharvest circulation process. AfterE-nose sampling, browning indexes, totalsoluble solid content, and titrable aciditywere recorded by sensory detection, a solublesolids refractometer, and acidity titration,respectively. The objectives of this researchwere to 1) to test the impact of mechanicalinjury and storage time on the quality ofpostharvest litchi; 2) to test the feasibility ofusing the E-nose to detect mechanical injuryof litchi; and 3) to find an efficient way todetect the quality of litchi fruit during storage.

Materials and Methods

Litchi samplesSamples of ‘Guiwei’ litchis for mechan-

ical injury detection experiments were har-vested at Conghua litchi orchard (located inGuangzhou, China) at 80% to 90% maturityand then shipped to the laboratory within 2 h.After their stems and leaves were removed,litchi samples were divided into three groups:injury-free, mild injury, and severe injury.The injury-free group did not undergo anytreatment. The mild and severe mechanicalinjury groups were dropped from heights of80 and 110 cm, respectively. Injury was notapparent on the mildly mechanically injuredlitchis; however, the severely mechanicallyinjured litchis had noticeable cracks on thepericarp. All litchi samples were packed inperforated polyethylene bags (300 mm ·200 mm · 0.05 mm; perforation ratio of5%). There were 10 litchi samples in eachbag, and the bags were stored at 25 �C(room temperature).

Samples of ‘Yuhebao’ litchis for storagequality detection experiments were alsoharvested at Conghua litchi orchard at 80%to 90% maturity and shipped to the labora-tory within 2 h. All litchi samples werestored in a cold environment (5 �C and90% humidity) after their stems and leaveswere removed. Before storage, all litchisamples were pre-cooled in 4 to 5 �C icewater for 5 min (Ruan et al., 2012) andpacked in perforated polyethylene bags(300 mm · 200 mm · 0.05 mm; perforation

ratio of 5%); there were 25 litchi samples ineach bag.

E-nose set-upA portable commercial E-nose (PEN3;

Airsense Inc., Schwerin, Germany) wasused to perform sampling of volatile li-tchis. This E-nose is mainly composed ofsampling and cleaning channels, a sensorarray, and data collection and processingsubsystems. The sensor array contains 10metal oxide gas sensors that are sensitiveto various volatiles, which makes the entireE-nose capable of detecting simple andcomplex odors. The parameters of those10 sensors are shown as Table 1 (Cardozoand Londo~no, 2013). The response dataof each sensor were represented as G/G0,where G was the response value of thesensor contacting the sample volatile andG0 was the response value of the sensorcontacting the zero gas (ambient air filteredthrough standard active carbon).

Experimental samplingE-nose sampling. Each litchi sample com-

prised one litchi fruit in a 100-mL glassbeaker that was sealed with double-layerpreservative film. It was placed in the storageenvironment immediately to avoid decayduring the volatile collection process. After0.5 h, the E-nose was applied to test theheadspace of each sample. Before each test,all sensors of the E-nose were cleaned andrestored by zero gas. Litchi samples for me-chanical injury detection were tested on days 0,1, 2, 3, 4, and 5 for each mechanical injurygroup; four samples were used for each testday. Litchi samples used for storage qualitydetectionwere tested on days 0, 3, 6, 10, 15, 19,and 24; 20 samples were used for each test day.

Sampling of quality parameters. Afterevery E-nose sampling, the browning degreesof litchi samples were evaluated using thesensory detection method (Jiang, 2000). Thefirst grade indicated that the brown areacomprised less than one-quarter of the fruit’stotal area. The second grade indicated that thebrown area comprised one-quarter or more ofthe fruit’s total area but less than one-half ofthe fruit’s total area. The third grade indi-cated that the brown area comprised one-halfor more of the fruit’s total area but lessthan three-quarters of the fruit’s total area.The fourth grade indicated that the brownarea comprised three-quarters or more of the

fruit’s total area. Therefore, the browningindex (BI) of a batch of litchis should becalculated as follows.

BI = SðL·NLÞ=M (1)

where L is the browning degree of a singlelitchi, NL is the amount of litchis with the Lth

grade, and M is the number of litchis in thebatch.

After detecting the browning degree, eachlitchi sample was peeled to acquire the flesh,which was then homogenized and filtered toobtain the litchi juice to determine the totalsoluble solid content (TSS) and titratableacidity (TA). The TSS of litchi samples wastested using a soluble solids refractometer(PR-32a; ATAGO Inc., Tokyo, Japan), andthe TA of litchi samples was determined asdescribed by previous research (AOAC Offi-cial Method 942.15, 1965; Jiang et al., 2004).The TA was defined as the percentage of citricacid determined by titration with 0.1MNaOH.

Data analysis methods. The linear dis-criminant analysis (LDA) (Gorjichakespariet al., 2016) is one of the most commonlyused classification procedures. This methodmaximizes the variance between categoriesand minimizes the variance within each sin-gle category. It usually has better classifica-tion ability than the principal componentanalysis and can show relationships amonggroups.

The K-nearest neighbors (KNN) method(Wang et al., 2017) is a nonparametricmethod based on the distance between ob-jects in a space with a dimension equal to thenumber of variables explored. The class towhich the sample is assigned is that of thesamples in the training group closest to it.Only the objects closest to K are used to makethe assignments.

The partial least-squares regression (PLSR)method (Tian et al., 2015) is a techniqueinvolving data containing correlated predic-tor variables. This technique constructs newpredictor variables, known as components, aslinear combinations of the original predictorvariables. PLSR constructs these componentswhile considering the observed responsevalues, thus leading to a parsimonious modelwith reliable predictive power. Currently,PLSR has been widely used for quantitativeanalysis modeling.

In this study, LDA was applied to deter-mine if the E-nose is able to detect mechanicalinjury of litchi fruit, determine the optimal

Table 1. Parameters of sensors of the PEN3 electronic nose

Sensor number Sensor name Substances for sensing Threshold value (mL·m–3)

R1 W1C Aromatics 10R2 W5S Nitrogen oxides 1R3 W3C Ammonia and aromatic molecules 10R4 W6S Hydrogen 100R5 W5C Methane, propane and aliphatic

nonpolar molecules1

R6 W1S Broad methane 100R7 W1W Sulfur-containing organics 1R8 W2S Broad alcohols 100R9 W2W Aromatics, sulfur- and

chlorine-containing organics1

R10 W3S Methane and aliphatics 10

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mechanical injury detection time for litchi,select the optimal sensors and feature values,and detect the storage time of litchi fruit. KNNwas used to further determine the detection

effect of the E-nose on storage time of litchi.PLSR was applied to determine the ability ofthe E-nose to quantify the quality parametersof litchi fruit during storage. Data statistics

were performed using Excel 2007 (MicrosoftCorporation, Redmond, WA). Data analysisand figure output were performed using Mat-lab 2017a (MathworksInc., Natick, MA).

Fig. 1. Influence of mechanical injury on storage qualities of litchi. (A) Browning index. (B) Total soluble solid. (C) Titratable acidity.

Fig. 2. Linear discriminant analysis (LDA) classification results of different mechanical injury degrees during storage. (A) Day 0. (B) Day 1. (C) Day 2. (D) Day 3.(E) Day 4. (F) Day 5.


Results

Mechanical injury affects litchi qualityparameters during storage

Changes in the quality of litchis afterdifferent mechanical injuries during storageare shown in Fig. 1. The BI of litchi increasedconstantly during storage, with severelymechanically injured litchis increasing thefastest, followed by mildly mechanically in-jured litchis and injury-free litchis (Fig. 1A).The TSS (Fig. 1B) and TA (Fig. 1C) ofinjury-free litchis changed slightly duringstorage (normal fluctuation, no obvious increaseor decrease). However, the TSS (Fig. 1B) andTA (Fig. 1C) of both mildly and severelymechanically injured litchis decreased withstorage time; severely mechanically injuredlitchis decreased the fastest, followed bymildly mechanically injured litchis. There-fore, mechanical injury not only acceleratesthe pericarp browning but also acceleratesflavor loss.

E-nose for mechanically injured litchiclassifications

LDA classifications based on E-nose datafor litchis with different degrees of mechan-ical injury during storage are shown in Fig. 2.The response data of E-nose sensors at 115 swere selected as the feature value for analy-sis. On storage day 0 (Fig. 2A), litchis withdifferent mechanical injuries can be classi-fied. However, injury-free litchis are similarto mildly mechanically injured litchis, whichmay lead to misclassification during detec-tion. From storage days 1 to 4 (Fig. 2B to E,respectively), all mechanical injury degrees oflitchi can be well-classified. The contribution

rate distributions are not uniform on the firstmain axis (LD1) or the second main axis(LD2), especially on day 1 (Fig. 2B); they are97.38% and 1.41%, respectively. However,the contribution rate distributions of LD1 andLD2 become more uniform with time afterinjury. They were 56.14% and 31.61%, re-spectively, on day 4 (Fig. 2E). The moreuniform the contribution rate distribution,the stronger the robustness of the detectionmodel. On storage day 5, mildly mechanicallyinjured litchis overlap with severely mechan-ically injured litchis, which cannot be classi-fied. Therefore, we can infer that mechanicalinjury of litchis can be well-detected duringdays 1 to 4 after injury; however, the best timeto detect mechanical injury of litchi is on day 4after injury during room temperature storage.

Normal litchi quality parameters changewith storage time

Changes in the quality parameters of nor-mal litchi during cold storage are shown inFig. 3. Similar changes occurred in injury-free litchis stored at room temperature, andthe BI of cold-stored litchis increased con-stantly with storage (Fig. 3A); however, itincreased slower than that of litchis stored atroom temperature. The TSS (Fig. 3B) and TA(Fig. 3C) of cold-stored litchis changedbarely during the entire storage period (days0–24), similar to the changes in injury-freelitchis stored at room temperature.

E-nose for normal litchi qualityparameter detection during storage

Elimination of singular sensor. An exam-ple of the sensor response of the E-nose tonormal ‘Guiwei’ litchi on day 0 is shown in

Fig. 4. The sensor (R7) reached themaximumvalue during 10 to 55 s of the samplingperiod, which may use the interference in-formation for the entire sampling period ofR7, even though the response value sup-ported the response range after 55 s.

The LDA classifications of the storagetime of litchi (Fig. 5) have further proven thehypothesis. The response data of E-nosesensors at 115 s were selected as the featurevalue for analysis. LDA classification resultsof the storage time of litchi based on theresponses of all sensors of the E-nose areshown in Fig. 5A. All storage days over-lapped with each other and cannot be classi-fied. LDA classifications of the storage timeof litchi based on the responses of all sensorsexcept R7 are shown in Fig. 5B. The inde-pendent characteristic of each storage day ismore evident than the LDA results in Fig. 5A,and on days 0 and 3 they can be well classi-fied with other storage days. We can furtherconfirm that R7 contains more inferentialclassification information than helpful infor-mation; therefore, R7 should be removedfrom the next data analysis.

Comprehensive feature extraction andstorage time detection. The raw data of theE-nose sensor response to litchi on storagedays 0, 6, 15, and 24 are shown in Fig. 6A, B,C, and D, respectively. The response valueincreased with storage time. The increasingrate of the response value (from 0 to 10 s) ofsensors R2, R6, R8, and R9 increased withstorage time. At 50 s, the response values ofthe sensors showed a different cross-status.Therefore, only using the 115-s responsevalue of each sensor cannot cover the entirechanged state of volatile litchi with storage.According to Fig. 6, the average differentialvalue of 0 to 10 s, the maximum value, the50s value, and the 115 s value were selected asthe comprehensive feature value for the nextanalysis.

After comprehensive feature value extrac-tion, LDA classification results of the storagetime of litchi are shown in Fig. 7. All storagedays can be classified. Therefore, the com-prehensive feature value indicates better in-formation regarding volatile litchi featurechanges than the single 115-s value duringstorage.

All litchis on different storage days can beclassified by LDA; however, some storagedays, such as days 10 and 15 and days 0 and 3are too close to each other. To further test the

Fig. 3. Normal litchi quality parameters change during storage. (A) Browning index change. (B) Total soluble solid change. (C) Titratable acidity change.

Fig. 4. Response of the electronic nose to litchi samples at day 0.


storage time detection effect of litchi basedon the E-nose, the KNN was used for thisresearch. Fifteen samples of each storage daywere selected randomly as the calibration set,and the remaining five samples were usedas the validation set. Therefore, the validationset had a total of 105 samples and the calibra-tion set had a total of 35 samples. For KNNdetection, the neighbor number (K) wouldaffect the detection effect, and an optimal Kvalue should be chosen by repeated attempts.After modeling, the optimal K value was 3,the detection accuracy of the calibration setwas 100%, and that of the validation set was91.43%.

PLSR for the litchi pericarp browningdegree, TSS, and TA determination. The re-sults of PLSR used to detect the degree oflitchi pericarp browning are shown in Fig. 8Aand B. There were 140 samples in total, and36, 38, 23, 20, and 23 litchi samples hadbrowning degrees of 1, 2, 3, 4, and 5, respec-tively. Furthermore, 27, 29, 18, 15, and 18samples with browning degrees from 1 to 5,respectively, were randomly selected as thecalibration set. Then, 9, 9, 5, 5, and 5samples with browning degrees of 1 to 5,respectively, were selected as the validationset. When predicting PLSR, to judge thecorrelation between the predicted and ac-tual values, it is necessary to fit the coeffi-cient (R2), as reported previously (Zhou andWang, 2011). The range of R2 is 0 to 1; thelarger the R2, the better the prediction effect.Therefore, the E-nose can effectively detectthe degree of litchi browning. The R2 valuesof calibration (Fig. 8A) and validation(Fig. 8B) for detecting the degree of brown-ing with PLSR are larger than 0.8.

The results of PLSR used to determinelitchi TSS and TA were are shown in Fig. 8 Cto F. Because TSS and TA barely changedduring storage, the TSS and TA values should

be uniformly distributed during the entirestorage period. However, considering thecomprehensiveness of the data that the de-tection model should possess, the detectionmodel should include sampling data of eachstorage day. There were a total of 140 sam-ples; 15 samples of each storage day wereselected randomly as the calibration set and 5samples were chosen as the validation set.Therefore, the validation set had a total of105 samples and the calibration set had a totalof 35 samples. The results of using PLSR todetect the TSS are shown in Fig. 8C, and thoseof the validation set are shown in Fig. 8D. Theresults of using PLSR to detect TA are shownin Fig. 8E, and those of the validation set areshown in Fig. 8F. The detected effects ofTSS and TA based on the E-nose were bothunsatisfied (all R2 < 0.4).

Discussion

Changes in quality parameters of litchidue to mechanical injury and storage. Colorfading and browning of litchi are mainlycaused by degradation of anthocyanin(Rivera-L�opez et al., 1999) and water lossin the pericarp (Jiang and Fu, 1999). De-creases in TSS and TA of litchi are mainlydue to respiration that consumes the nutrientsubstances of fresh litchi (Feng et al., 2011).The pericarp color fades significantly duringstorage; however, previous research foundthat the flavor of litchi is less changed (Jianget al., 2006), which is in agreement with theresults of this research. Our results also foundthat the quality of litchi fruit with mechanicalinjury decreases faster than that of injury-freeones, and that litchis with heavier mechanicalinjury experience faster degrees of browningand decreased rates of TSS or TA comparedto those with lighter mechanical injury. Thiscould be explained by previous research thatindicated that mechanical injury increaseswater loss and respiration of litchi fruit (Chenet al., 2013a).

E-nose for mechanical injury detection oflitchi fruit. Mechanical injury can break fruittissues and increase the release of ethylene,carbon dioxide, and secondary metabolites

Fig. 5. Linear discriminant analysis (LDA) classification results of litchi storage time based on all E-nose sensors (A). A singular sensor (R7) was removed fromthe electronic nose (E-nose) sensors (B).

Fig. 6. Raw data of the electronic nose (E-nose) response to litchi samples on different storage days. (A)Day 0. (B) Day 6. (C) Day 15. (D) Day 24.

Fig. 7. Linear discriminant analysis (LDA) classi-fication results of litchi storage time after fea-ture extraction.


(Wang et al., 2007). Therefore, mechanicalinjury of litchi can be detected by the E-nose.This research indicated that mechanical in-jury of litchi can be well-classified duringdays 1 to 4 after injury, cannot be classifiedon day 5, and is best detected on day 4. Thismay be because the variety and concentrationof injury of volatiles increased with the stor-age time after injury; however, volatiles withmild mechanical injury were similar to thosewith severe mechanical injury on storageday 5. Gas chromatography-mass spectrom-eter combined with E-nose should be appliedin further studies to explore the details ofvolatile changes after mechanical injury.

Sensor optimization and feature extraction.The successful use of the E-nose for foodquality detection has been proven by manystudies (Natale et al., 1997). During E-nosedetection, not all sensors are useful for classi-fication. Some sensors contain more interfer-ence information than useful information, suchas the R7 for ‘Yuhebao’ litchi sampling duringthis experiment, and should be removed beforedata analysis (Shi et al., 2013). The E-noseresponse value of ‘Guiwei’ litchi did not reachthemaximumvalue, but the response data of allsensors have been kept for future data analysis.In addition, previous research proved that mul-tiple features can discover more comprehensiveinformation of samples than a single feature,thereby providing better detection effects (Weiet al., 2015). This research suggested the use ofan average differential value of 0 to 10 s, themaximum value, the 50-s value, and the 115-svalue to construct the comprehensive featurevalue for litchi storage quality detection

based on the E-nose. Therefore, E-nose datapreprocessing differs according to the litchivariety and detection target.

Litchi storage time, browning degree,TSS, and TA determination. Litchi volatilesare changed during storage, as proven by ourprevious research, thereby providing the the-oretical basis for using the E-nose to detectquality parameters of litchi during storage(Xu et al., 2016). Our previous researchresults showed that alkene, alcohols, and ke-tones exist in litchi volatiles. The concentra-tion of alkene was the highest and increasedconstantly during storage. Other aromaticcomponents decreased during storage. Thisresearch further indicated that the E-nose isable to predict the storage time and browningdegree of litchi but cannot accurately predictthe TSS and TA of litchi during storage. Thismight be because both litchi volatiles anddegrees of browning change with increasedstorage time; that is, there are strong rela-tionships between E-nose data and storagetime and browning degrees. However, withincreased storage time, litchi volatiles changeda lot, as did TSS and TA, indicating that therelationships between E-nose data and TSSand TA are very weak.

Litchi fruit supervision during the circula-tion process. During the circulation process,litchi fruit can easily sustain mechanical injuryduring the harvest and transportation stagesand quickly decay during the storage stage.Litchis with mechanical injury should beremoved at the transportation stage or earlystorage stage. Stored litchis should be soldbefore reaching a certain degree of browning.

Litchis with significant browning should bediscarded. Because the TSS and TA of litchifruit do not change much during the circula-tion process, their supervision is unnecessary.According to our experimental results, me-chanical injury of litchi fruit could be detectedby the E-nose with the LDA method, thestorage time of litchi fruit could be detectedby the E-nose with the KNN method, and thebrowning degree of litchi fruit could be de-tected by the E-nose with the PLSR method.Therefore, the E-nose could be a feasible toolfor supervising litchi fruit during the circula-tion process.

Conclusions

This study used the E-nose (PEN 3) todetect the quality of postharvest litchi fruits,detect mechanical injury, and detect storagequality. The E-nose may be an effective toolfor litchi fruit supervision during the circula-tion process. The experimental results wereas follows:

(1) The BI, TSS, and TA of both mildly andseverely mechanically injured litchisdecreased with storage time. The qual-ity of litchis with severe mechanicalinjury decreased the fastest, followedby litchis with mild mechanical injury.The BI quickly increased during storage;however, the TSS and TA of injury-freeand normal litchis changed slightly dur-ing room temperature and cold storage.

(2) To detect mechanical injury of ‘Guiwei’litchi fruit, E-nose sensor response valuesat 115 s could be selected as feature valuesfor analysis. However, to detect storagequality of ‘Yuhebao’ litchi fruit, sensor R7should be removed to avoid interference.The average differential value of 0 to 10 s,the maximum value, the 50-s value, and the115-s value are also recommended for con-structing a comprehensive feature valuefor data analysis. E-nose data preprocess-ing differs according to the litchi varietyand detection target.

(3) Mechanical injury of litchis can be well-classified by the E-nose with LDA duringday 1 to day 4 after injury. The best time todetect mechanical injury is at day 4 afterinjury during room temperature storage.

(4) Litchi storage time can be classified by theE-nose with LDA; however, some storagetimes are too close to each other, whichmay lead to misclassification during prac-tical storage time. KNN further proved thefeasibility of using the E-nose to detectlitchi storage time with detection accuracyrates of 100% and 91.43%, respectively,for the calibration set and validation set.The degree of browning can be detected bythe E-nose with PLSR, with R2 values of0.8582 and 0.08015, respectively, for thecalibration set and validation set. However,the TSS and TA cannot be satisfactorilydetected by the E-nose (R2 value <0.4for the calibration set and the validationset).

Fig. 8. Partial least-squares regression (PLSR) detection results of litchi pericarp browning degree (A andB), total soluble solid (TSS) (C and D), and titratable acidity (TA) (E and F) detection. (A, C, E)Detection results of calibration sets. (B, D, F) Detection results of validation sets.


(5) Supervision should be performed duringthe circulation process using the E-nose. Li-tchis with mechanical injury should be re-moved at the transportation stage or earlystorage stage.Litchis in storage shouldbe soldbefore reaching a certain degree of browning.Litchis with significant browning should bediscarded. Because the TSS and TA of litchifruit barely change during the circulationprocess, their supervision is unnecessary.

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