6 research outputs found
Robust model of fresh jujube soluble solids content with near-infrared (NIR) spectroscopy
A robust partial least square (PLS) calibration model with high accuracy and stability was established for the measurement of soluble solids content (SSC) of fresh jujube using near-infrared (NIR) spectroscopytechnique. Fresh jujube samples were collected in different areas of Taigu and Taiyuan cities, central China in 2008 and 2009. A partial least squares (PLS) calibration model was established based on the NIR spectra of 70 fresh jujube samples collected in 2008. A good calibration result was obtained with correlation coefficient (Rc) of 0.9530 and the root mean square error of calibration (RMSEC) of 0.3951 °Brix. Another PLS calibration model was established based on the NIR spectral of 180 samples collected in 2009; it resulted in the Rc of 0.8536 and the RMSEC of 1.1410 °Brix. It could be seen that the accuracy of established PLS models were different when samples harvested in different years were used for the model calibration. In order to improve the accuracy and robustness of model, different numbers (5, 10, 15, 20, 30 and 40) of samples harvested in 2008 were added to the calibration sample set of the model with samples harvested in 2009, respectively. The established PLS models obtained Rc with the range of 0.8846 to 0.8893 and RMSEC with the range of 1.0248 to 0.9645 °Brix. The obtained results werebetter than the result of the model which was established only with samples harvested in 2009. Moreover, the models established using different numbers of added samples had similar results. Therefore, it was concluded that adding samples from another harvest year could improve the accuracy and robustness of the model for SSC prediction of fresh jujube. The overall results proved that the consideration of samples from different harvest places and years would be useful for establishing an accuracy and robustness spectral model.Keywords: Near-infrared (NIR) spectroscopy, Huping jujube, soluble solids content (SSC), partial least squares (PLS), accuracy, stabilit
Nondestructive Assessment of Citrus Fruit Quality and Ripening by Visible–Near Infrared Reflectance Spectroscopy
As non-climacteric, citrus fruit are only harvested at their optimal edible ripening stage. The usual approach followed by producers and packinghouses to establish the internal quality and ripening of citrus fruit is to collect fruit sets throughout ripening and use them to determine the quality attributes (QA) by standard and, in many cases, destructive and time-consuming methods. However, due to the large variability within and between orchards, the number of measured fruits is seldom statistically representative of the batch, resulting in a fallible assessment of their internal QA (IQA) and a weak traceability in the citrus supply chain. Visible/near-infrared reflectance spectroscopy (Vis–NIRS) is a nondestructive method that addresses this problem, and has proved to predict many IQA of a wide number of fruit including citrus. Yet, its application on a daily basis is not straightforward, and there are still several questions to address by researchers in order to implement it routinely in the crop supply chain. This chapter reviews the application of Vis–NIRS in the assessment of the quality and ripening of citrus fruit, and makes a critical evaluation on the technique’s limiting issues that need further attention by researchers
Multivariate analysis and artificial neural network approaches of near infrared spectroscopic data for non-destructive quality attributes prediction of Mango (Mangifera indica L.)
There is a need for fast and reliable quality and authenticity control tools of pharmaceutical ingredients. Among others, hormone containing drugs and foods are subject to scrutiny. In this study, terahertz (THz) spectroscopy and THz imaging are applied for the first time to analyze melatonin and its pharmaceutical product Circadin. Melatonin is a hormone found naturally in the human body, which is responsible for the regulation of sleep-wake cycles. In the THz frequency region between 1.5 THz and 4.5 THz, characteristic melatonin spectral features at 3.21 THz, and a weaker one at 4.20 THz, are observed allowing for a quantitative analysis within the final products. Spectroscopic THz imaging of different concentrations of Circadin and melatonin as an active pharmaceutical ingredient in prepared pellets is also performed, which permits spatial recognition of these different substances. These results indicate that THz spectroscopy and imaging can be an indispensable tool, complementing Raman and Fourier transform infrared spectroscopies, in order to provide quality control of dietary supplements and other pharmaceutical products
Non-destructive evaluation of external and internal table grape quality
Thesis (PhDAgric)--Stellenbosch University, 2021.ENGLISH ABSTRACT: Determining the correct harvest maturity parameters of table grapes is an essential step before
harvesting. The chemical analysis of table grapes to determine harvest and quality parameters
such as total soluble solids (TSS), titratable acidity (TA) and pH, is very time-consuming,
expensive, and destructive. Developing faster and more cost-effective methods to obtain the
information can benefit the table grape industry by reducing losses suffered at the postharvest
stage. There are multitudes of factors that can influence table grape postharvest quality leading
to huge losses. These losses are exacerbated even further by the long list of postharvest
external and internal defects that can occur, including browning in all its various manifestations.
The application of cutting-edge technologies such as Fourier Transform Near-Infrared (FT-NIR)
spectroscopy that can accurately assess the external and internal quality of fruit is, therefore,
essential. This particularly concerns the identification of defects or assessment of the risks of
defects that are likely to develop during post storage. The aim of this application would thus be
to evaluate these new technologies to monitor table grape quality non-destructively, before,
during, and/or after harvest.
This study, therefore, focussed on the development and optimisation of faster, cost-
effective, and fit-for-purpose methods to monitor harvest maturity and quality of table grapes in
the vineyard before harvesting and during packaging and cold storage. Harvest of three
different cultivars, namely, Thompson Seedless, Regal Seedless and Prime, happened over two
seasons (2016 and 2017) from six different commercial vineyards. Five of these vineyards were
in the Western Cape (two in the Hex River Valley, three in Wellington) and one in the Northern
Cape (Kakamas), South Africa. Harvest occurred twice at each vineyard, at optimum ripeness
and two weeks later (after the optimum harvest date). The incidence and intensity of browning
on each berry on a bunch were evaluated for different defects and browning phenotypes.
Quantitative harvest maturity and indicative quality parameters such as TSS, TA and pH, as well
as the sensory-related parameters – sugar:acid ratio (TSS:TA ratio) and BrimA, were
investigated by scanning whole table grape bunches contactless with Bruker’s MATRIX-F
spectrometer in the laboratory. Partial Least Squares (PLS) regression was used to build
prediction models for each parameter. Two different infrared spectrometers, namely the Bruker
Multipurpose Analyser Fourier Transform Near-Infrared (MPA FT-NIR) and MicroNIR Pro 1700
were also used to determine TSS on whole table grape berries. The MicroNIR Pro 1700 was
utilised in the vineyard and the laboratory and the MPA only in the laboratory. The same
spectral dataset used to build the quantitative models was used to build classification models for
two browning phenotypes, namely chocolate browning and friction browning. Partial Least
Squares Discriminant Analysis (PLS-DA) and Artificial Neural Networks (ANN) were used for the
classification tasks.
Key results showed that the incidence and intensity of different defects and browning
phenotypes such as sulphur dioxide (SO2) damage were prevalent on all three white seedless
table grape cultivars. The incidences of fungal infection, sunburn and abrasion damage were
high on Regal Seedless and Thompson Seedless in 2016. Contact browning, mottled browning
and friction browning and bruising damage had higher incidences in 2017 than in 2016. Overall,
the intensity of defects was very high in 2016 except on Regal Seedless from Hex River Valley.
Prime from Kakamas and Wellington had the highest intensity of defects in 2017, which
appeared on the grapes after 7 weeks of cold storage.
Prediction models were successfully developed for TSS, TA, TSS:TA, pH, and BrimA minus
acids on intact table grape bunches using FT-NIR spectroscopy in a contactless measurement
mode, and applying spectral pre-processing techniques for regression analysis with PLS. The
combination of Savitzky-Golay first derivative coupled with multiplicative scatter correction on
the original spectra delivered the best models. Statistical indicators used to evaluate the models
were the number of latent variables (LV) used to build the model, the prediction correlation
coefficient (R2p) and root mean square error of prediction (RMSE). For the respective
parameters TSS, TA, TSS:TA ratio, pH, and BrimA, the number of LV used when the models
were build according to a random split of the calibration and validation set were 6, 4, 5, 5 and
10, the R2p = 0.81, 0.43, 0.66, 0.27, and 0.71, and the RMSEP = 1.30 °Brix, 1.09 g/L, 7.08,
0.14, and 1.80. When 2016 was used as the calibration set and 2017 as the validation set in
model building the number of LV used were 9, 5, 5, 4 and the R2p = 0.44, 0.06, 0.17, 0.05, and
0.05 and the RMSEP = 3.22 °Brix, 2.41 g/L, 14.53, 0.21, and 8.03 for for the respective
parameters.
Determining TSS of whole table grape berries in the vineyard before and after harvesting
using handheld and benchtop spectrometers on intact table grape berries showed that spectra
taken in the laboratory with the MicroNIR were more homogenous than those taken in the
vineyard with the same spectrometer, over the two years investigated. The results obtained with
the MPA were not as good as those obtained with the MicroNIR in the laboratory were. The
model constructed with the combined data of 2016 and 2017 taken in the laboratory with the
MicroNIR had the best statistics in terms of R2p (0.74) and RPDp (1.97). The model constructed
with the 2017 data obtained in the laboratory with the MicroNIR had the lowest prediction error
(RMSEP = 1.13°Brix).
Good models were obtained using PLS-DA and ANN to classify bunches as either clear or
as having chocolate browning and friction browning based on the spectra obtained from intact
table grape bunches with the MATRIX-F spectrometer. The classification error rate (CER),
specificity and sensitivity were used to evaluate the models constructed using PLS-DA and the
kappa score was used for ANN. The CER for chocolate browning (25%) was better than that of
friction browning (46%) after Weeks 3 and 4 in cold storage for both class 0 (absence of
browning) and class 1 (presence of browning). Both the specificity and sensitivity of class 0 and
class 1 of friction browning were not as good as for chocolate browning. With ANN, the testing
kappa score to classify table grape bunches as clear or having chocolate browning or friction
browning showed that chocolate browning could be classified with the strong agreement after
Weeks 3 and 4 and Weeks 5 and 6 and that friction browning could be classified with moderate
agreement after three and four weeks in cold storage. Classification of chocolate browning and
friction browning phenotypes was done using PLS-DA and ANN and the result showed that both
types of browning can be classified with moderate agreement.
The implications of the results of this study for the table grape industry are that the industry
can move beyond just assessing methods and techniques in the laboratory towards
implementation in the vineyard and the packhouse. Much quicker decisions regarding grape
quality and destination of export can now be made using a combination of the MicroNIR
handheld and MATRIX-F instruments for onsite quality measurement and the models to predict
internal (e.g. TSS) and external (browning) quality attributes.AFRIKAANSE OPSOMMING: Die bepaling van die korrekte oesrypheidsparameters van tafeldruiwe is 'n noodsaaklike stap
voor oes. Chemiese ontleding van tafeldruiwe om oes- en kwaliteitsparameters te bepaal, soos
totale oplosbare vaste stowwe (TOVS), titreerbare suur (TS) en pH, is baie tydrowend, duur en
vernietigend. Die ontwikkeling van vinniger en kostedoeltreffender maniere om die inligting te
bekom, kan die tafeldruifbedryf bevoordeel deur verliese wat in die na-oesstadium gely word, te
verminder. Dit sluit die menigte faktore in wat die gehalte van tafeldruiwe ná oes kan beïnvloed
en tot verliese lui. Hierdie verliese word nog verder vererger deur die lang lys van verskillende
na-oes-verwante gebreke wat kan voorkom, insluitend verbruining in al sy verskillende
manifestasies. Die toepassing van toonaangewende tegnologieë soos Fourier-transform-naby-
infrarooi (FT-NIR) spektroskopie wat die eksterne en interne kwaliteit van vrugte akkuraat kan
beoordeel, is dus noodsaaklik. Dit is veral die identifisering van gebreke, of die beoordeling van
die risiko's van gebreke, wat waarskynlik tydens die opberging kan ontstaan. Die doel van
hierdie toepassing was dus om hierdie nuwe tegnologieë te evalueer om die kwaliteit van
tafeldruiwe nie-vernietigend te monitor, voor, tydens en/of ná oes.
Hierdie studie het dus gefokus op die ontwikkeling en optimalisering van vinniger, koste-
effektiewe en geskikte doeleindes om oesrypheid en kwaliteit van tafeldruiwe in die wingerd te
monitor voor oes en tydens verpakking en koelopberging. Druiwe-oes van drie verskillende
kultivars (Thompson Seedless, Regal Seedless en Prime) het gedurende twee jare (2016 en
2017) uit ses verskillende kommersiële wingerde plaasgevind. Vyf van hierdie wingerde was in
die Wes-Kaap (twee in die Hexriviervallei, drie in Wellington) en een in die Noord-Kaap
(Kakamas), Suid-Afrika. Die oes het twee keer by elke wingerd plaasgevind, dit wil sĂŞ op die
beste rypheid en twee weke later ná die optimale oesdatum. Die voorkoms en intensiteit van
verbruining op elke korrel op 'n tros is op verskillende defekte en verbruiningsfenotipes
geëvalueer. Kwantitatiewe oesrypheid en kwaliteitsindikatiewe parameters, naamlik TOVS, TS
en pH, sowel as sensoriese verwante parameters suiker:suur-verhouding (TOVS:TS-
verhouding) en BrimA is ondersoek deur heel tafeldruiftrosse sonder kontak met die Bruker se
MATRIX-F-spektrometer in die laboratorium te skandeer. Gedeeltelike minste kwadrate (GMK)
regressie is gebruik om modelle vir die parameters te bou. Twee verskillende infrarooi-
spektrometers naamlik (a) die Bruker Multipurpose Analyzer Fourier Transform Near-Infrared
(MPA FT-NIR) en (b) MicroNIR Pro 1700 is ook gebruik om TOVS op heel tafeldruifkorrels te
bepaal. Die MicroNIR Pro 1700 is in die wingerd en in die laboratorium gebruik en die MPA
slegs in die laboratorium. Met behulp van dieselfde spektrale datastel as die een wat gebruik
word om die kwantitatiewe modelle op te stel, is klassifikasiemodelle vir twee verskillende
verbruiningsfenotipes (sjokoladeverbruining en wrywingverbruining) gebou. Hierdie keer is
gedeeltelike minste-kwadrate-diskriminant-analise (GMK-DA) en kunsmatige neurale netwerke
(KNN) gebruik.
Die belangrike resultate het getoon dat die voorkoms en intensiteit van verskillende defekte
en verbruiningsfenotipes soos swaeldioksied (SO2)-skade op al drie wit pitlose tafeldruifkultivars
voorgekom het. Die voorkoms van swaminfeksie, sonbrand en skaafskuur was hoog op Regal
Seedless en Thompson Seedless in 2016. Kontak-, gevlekte- en wrywing verbruining sowel as
kneusplekke het in 2017 'n hoër voorkoms as in 2016 gehad. Oor die algemeen was die
intensiteit van defekte baie hoog in 2016 behalwe op Regal Seedless vanaf die Hexriviervallei.
Prime van Kakamas en Wellington het in 2017 die hoogste intensiteit van gebreke gehad wat
ná 7 weke se koelopberging op die druiwe verskyn het.
Die suksesvolle ontwikkeling van modelle vir TOVS, TS, TOVS:TS verhouding, pH en
BrimA op heel tafeldruiftrosse met behulp van FT-NIR-spektroskopie is bewys as inderdaad
moontlik – veral as GMK met verskillende spektrale voorverwerkingstegnieke gepaard gaan.
Statistiese aanwysers wat gebruik is om die modelle te evalueer, was die aantal latente
veranderlikes (LV) wat gebruik is om die model te bou, die voorspellingskorrelasiekoëffisiënt
(R2p) en wortelgemiddelde vierkante voorspellingsfout (WGVVF). Die kombinasie van die eerste
afgeleide Savitzky-Golay tesame met die vermenigvuldigende verstrooiingskorreksie op die
oorspronklike spektra het die beste modelle gelewer. Statistiese aanwysers wat gebruik is om
die modelle te evalueer, was die aantal latente veranderlikes (LV) wat gebruik is om die model
te bou, die voorspellingskorrelasiekoëffisiënt (R2p) en wortelgemiddelde vierkante
voorspellingsfout (RMSE). Vir die onderskeie parameters TSS, TA, TSS: TA-verhouding, pH en
BrimA, was die aantal LV wat gebruik is toe die modelle volgens 'n ewekansige verdeling van
die kalibrasie- en valideringstel gebou is, 6, 4, 5, 5 en 10, die R2p = 0,81, 0,43, 0,66, 0,27 en
0,71, en die RMSEP = 1,30 ° Brix, 1,09 g / l, 7,08, 0,14 en 1,80. Toe 2016 as die kalibrasiestel
gebruik is en 2017 as die validasieset in modelbou, was die aantal gebruikte LV 9, 5, 5, 4 en die
R2p = 0,44, 0,06, 0,17, 0,05 en 0,05 en die RMSEP = 3,22 ° Brix, 2,41 g / l, 14,53, 0,21 en 8,03
vir die onderskeie parameters. Die bepaling van TOVS van heel tafeldruifkorrels in die wingerd
voor en ná oes oor twee jaar met behulp van hand- en tafelbladspektrometers het getoon dat
spektra wat in die laboratorium met die MicroNIR geneem is meer homogeen was as dié wat in
die wingerd met dieselfde spektrometer geneem is. Die resultate wat met die MPA behaal is,
was nie so goed soos met die MicroNIR in die laboratorium nie. Die model wat saamgestel is
met die gekombineerde data van 2016 en 2017 wat in die laboratorium met die MicroNIR
geneem is, het die beste statistieke gehad in terme van die R2p (0.74) en die RPDp (1.97). Die
model wat opgestel is met die 2017 data wat in die laboratorium met die MicroNIR verkry is, het
die laagste voorspellingsfout (RMSEP = 1.13°Brix) gehad.
Goeie modelle is verkry met behulp van GMK-DA en KNN om trosse as skoon te
klassifiseer, of as sjokoladeverbruining en wrywingsverbruining gebaseer op die spektra van die
heel tafeldruiftrosse wat met die MATRIX-F-spektrometer geneem is. Die klassifikasiesyfer
(KS), spesifisiteit en sensitiwiteit is gebruik om die modelle wat met behulp van GMK-DA
saamgestel is, te evalueer en die kappa-telling is vir KNN gebruik. Die KS vir
sjokoladeverbruining (25%) was beter as dié van wrywingsverbruining (46%) vir week 3 en
week 4 vir beide klas 0 (afwesigheid van verbruining) en klas 1 (teenwoordigheid van
verbruining). Beide die spesifisiteit en sensitiwiteit van klas 0 en klas 1 vir wrywingverbruining
was nie so goed soos vir sjokoladeverbruining nie. Met KNN het die toetskappa-telling om
tafeldruiftrosse as skoon of sjokoladeverbruining of wrywingsverbruining te klassifiseer, getoon
dat sjokoladeverbruining tydens Week 3 en Week 4 en Week 5 en Week 6 met 'n matige
ooreenstemming geklassifiseer kan word en dat wrywingsverbruining met matige
ooreenstemming tydens Week 3 en Week 4 geklassifiseer kan word.
Die implikasies van hierdie resultate vir die tafeldruifbedryf is van so 'n aard dat die bedryf
nou verder kan gaan as om net metodes en tegnieke in die laboratorium te beoordeel, maar kan
beweeg na implementering in die wingerd en die pakhuis. Die neem van baie vinniger besluite
rakende die kwaliteit van die druiwe, dit wil sĂŞ in watter klas druiwe geplaas kan word en na
watter uitvoermark druiwe gestuur kan word, is nou moontlik. Veel vinniger besluite rakende
druiwekwaliteit en bestemming van uitvoer kan nou geneem word met behulp van 'n kombinasie
van die MicroNIR-hand- en MATRIX-F-instrumente vir kwaliteitsmeting in situ en die modelle om
interne (bv. TOVS) en eksterne (verbruining) kwaliteitseienskappe te voorspel.Doctora
Application of LS-SVM and Variable Selection Methods on Predicting SSC of Nanfeng Mandarin Fruit
International audienceThe objective of this research was to investigate the performance of LS-SVM combined with several variable selection methods to assess soluble solids content (SSC) of Nanfeng mandarin fruit. Visible/near infrared (Vis/NIR) diffuse reflectance spectra of samples were acquired by a QualitySpec spectrometer in the wavelength range of 350~1800 nm. Four variable selection methods were conducted to select informative variables for SSC, and least squares-support vector machine (LS-SVM) with radial basis function (RBF) kernel was used develop calibration models. The results indicate that four variable selection methods are useful and effective to select informative variables, and the results of LS-SVM with these variable selection methods are comparable to the results of full-spectrum partial least squares (PLS). Genetic algorithm (GA) combined with successive projections algorithm (SPA) is the best variable selection method among these four methods. The correlation coefficients and RMSEs in LS-SVM with GA-SPA model for calibration, validation and prediction sets are 0.935, 0.560%, 0.912, 0.631% and 0.933, 0.594%, respectively