红外光谱法作为一种快速的工具进行分类对水果的属性强度的基础上榨橄榄油

Application of near (NIR)infrared and mid (MIR)infrared spectroscopy as a rapid tool to classify extra virgin olive oil on the basis of fruity attribute intensity

Nicoletta Sinelli a,*,Lorenzo Cerretani b,*,Valentina Di Egidio a ,Alessandra Bendini b ,Ernestina Casiraghi a

a Dipartimento di Scienze e Tecnologie Alimentari e Microbiologiche,Universitàdegli Studi di Milano,Via Celoria 2,I-20133Milano,Italy b

Dipartimento di Scienze degli Alimenti,Universitàdi Bologna,P.zza Goidanich 60,I-47521Cesena (FC),Italy

a r t i c l e i n f o Article history:

Received 21July 2009

Accepted 22October 2009

Keywords:

Virgin olive oil Classi?cation Sensory analysis NIR spectroscopy MIR spectroscopy

a b s t r a c t

A sensory analysis of 112virgin olive oils was performed by a fully trained taste panel.The samples were divided in ‘‘defective”and ‘‘not defective”on the basis of their olfactory attributes.Then,the ‘‘not defec-tive”samples were classi?ed into ‘‘low”,‘‘medium”and ‘‘high”according to the fruity aroma intensity perceived by assessors.All samples were also analysed by FT-NIR and FT-IR spectroscopy and processed by classi?cation methods (LDA and SIMCA).The results showed that NIR and MIR spectroscopy coupled with statistical methods are an interesting technique compared with traditional sensory assessment in classifying olive oil samples on the basis of the fruity attribute.The prediction rate varied between 71.6%and 100%,as average value.The spectroscopic methods,combined with chemometric strategies,could represent a reliable,cheap and fast classi?cation tool,able to draw a complete ?ngerprint of a food product,describing its intrinsic quality attributes,that include its sensory attributes.

ó2009Elsevier Ltd.All rights reserved.

1.Introduction

Olive oil legislation is in a continuous updating phase with the aim both to guarantee the product quality belong to different com-mercial categories (extra virgin,virgin and lampante)and to iden-tify possible sophistication and illegally treatments.

The sensory analysis is one of the most important tools useful to protect the quality of the virgin olive oil.This approach has been proposed in June 1987by the International Olive Council (Interna-tional Olive Council,1987)and recognized by the European Com-munity in July 1991(EEC Reg.,2568/1991).The method provided in Annex XII (EEC Reg.,2568/1991)was then replaced by the new method (COI/T.20/Doc.No.15)for the organoleptic assess-ment of virgin olive oils (International Olive Council,1996),and recognized by the European Community (EC Reg.,796/2002)more reliable and simpler.Afterwards,this last method was modi?ed in September 2007(International Olive Council,2007)permitting the use of optional terminology for labeling purposes (EC Reg.,1019/2002).In fact,upon request,the panel leader may certify that the oils which have been assessed comply with the de?nitions and the intensity ranges for the three positive attributes fruity,bitter and pungent.These last indications avoid the indiscriminate use of not corrected de?nitions in the label that may confuse the con-sumer.The adjective permitted for labeling use concerning the fru-ity attribute are:

–light,when the median of the fruitiness intensity is less than 3in a scale of 10cm;

–medium,when the median of the fruitiness intensity is between 3cm and 6cm in a scale of 10cm;

–intense,when the median of the fruitiness intensity is more than 6cm in a scale of 10cm.This approach is time-consuming and not always practical for large-scale commercial purposes as involved the use of trained sensory panellists or individual.Considerable interest exists in the development of instrumental techniques,non-invasive and non-destructive,in order to make more objective,faster and less expensive the assessments of olive oil sensory quality.

In the last years the use of molecular spectroscopic techniques,such as near infrared spectroscopy (NIR)and mid infrared spec-troscopy (MIR),associated with the chemometric methods has been recognised in various analytical application for evaluating the olive oil composition.It is well known that MIR and NIR spec-troscopy can be applied as alternative method for quantitative measurement of acidity and peroxide value (Ahmed,Daun,&Pry-bylski,2005;Azizian &Kramer,2005)and for the determination of fatty acid composition (Maggio et al.,2009).These techniques have been successfully deployed in adulteration detection of virgin olive oil by different vegetable oils (Banu &Mauer,2002;Ozdemir &Ozturk,2007)and in authentication studies of olive oil on the basis

0963-9969/$-see front matter ó2009Elsevier Ltd.All rights reserved.doi:10.1016/j.foodres.2009.10.008

*Corresponding authors.Tel.:+390250319179;fax:+390250319190(N.Sinelli),tel.:+390547338121;fax:+390547382348(L.Cerretani).

E-mail addresses:Nicoletta.Sinelli@unimi.it (N.Sinelli),lorenzo.cerretani@unibo.it (L.Cerretani).

Food Research International 43(2010)

369–375

Contents lists available at ScienceDirect

Food Research International

j o ur na l h om e pa ge :w w w.e ls e v ie r.c om /lo c at e /f oo dr e

s

of geographical origin(Bendini et al.,2007;Casale,Casolino,Ferrari,& Forina,2008;Galtier et al.,2008;Sinelli,Casiraghi,Tura,&Downey, 2008;Tapp,Defernez,&Kemsley2003).Nevertheless there are no references regarding the application of these techniques to clas-sify the extra virgin olive oil on the basis of their sensory pro?les. Recently,only few authors have investigated the relationship between the sensory quality of food and NIR spectroscopic data. In particular,NIR method has been compared with a sensory anal-ysis in order to predict the eating quality attributes of cooked rice (Srisawas,Jindal,&Thanapase,2007;Qingyun,Yeming,Mikami, Kawano,&Zaigui,2007).Others authors have also investigated the correlation between spectral data and sensory assessment of wine(Cozzolino et al.,2008),of different types of cheeses(Cattaneo, Tornelli,Erini,&Panarelli,2008;Downey et al.,2005)and of beef steaks during the aging(Liu et al.,2003).On the other hand,there are no works about the relationship between spectral data of extra virgin olive oil and its sensory attributes.

The purpose of this study was to examine the feasibility of FT-IR and FT-NIR spectroscopy,combined with chemometric data analy-sis,to classify different samples of extra virgin olive oils on the ba-sis of the fruity sensory attribute.

2.Materials and methods

2.1.Samples

The virgin olive oils(n=112)used in this experimentation came from several industrial olive mills located in different Medi-terranean area with a prevalence of samples coming from Italy. Speci?c information on sample origin is reported in Table1.The randomized provenience of samples has been decided in order to have different oils,in terms of olive cultivar,area of olive growing, technological plant for olive oil production and storage condition.

All oils were produced during a single crop season:2007–2008. For the sensory evaluation,the samples have been sent by each manufacturer to Olio Capitale international olive oil award.

2.2.Sensory analysis

Sensory analysis was performed by an Of?cial Panel recognized by the Italian Ministry in2006.The Panel Professionale del Diparti-mento di Scienze degli Alimenti dell’Universitàdi Bologna(Cesena, Italy)was composed by16judges(mean age36years),subse-quently divided into two groups of eight assessors.Each group smelt a maximum of14oils,randomly selected among the112 samples.All oils were analysed during4days.Moreover,to reduce the number of tests a balanced incomplete block design was ap-plied according to Cochran and Cox(1957).

For the present study a standard pro?le sheet realized according to IOC method T20was used(International Olive Council,2007). Each assessor rated the intensity of the sensory attributes using a 10cm continuous line.Table2shows the sensory lexicon used to evaluate virgin olive oils.This sensory ballot has been considered only for olfactory attributes.To de?ne a sample as defective at least 80%of agreement among judges was required.The fruity attribute intensity has been classi?ed into three levels:‘‘intense”,‘‘medium”,‘‘light”when the median of the attribute was greater than6,be-tween3and6and less than3,respectively(EC Reg.,640/2008).

2.3.Instruments

2.3.1.FT-NIR spectrometer

NIR spectral data were collected in transmission mode using vials of8mm path length with an FT-NIR spectrometer(MPA,Bru-ker Optics,Ettlingen,Germany).The spectral data were collected over the range12,500–4500cmà1(resolution8cmà1,scanner velocity:10kHz,background:64scans,sample:64scans)at room temperature.Instrument control and initial data processing were performed using OPUS software(v.6.5Bruker Optics,Milan,Italy).

2.3.2.FT-IR spectrometer

ATR FT-IR experiments were performed by a spectrometer (ALPHA,Bruker Optics,Ettlingen,Germany)equipped with a deuterated triglycine sulfate(DTGS)detector.

The spectral data were collected over the range4000–400cmà1 (resolution4cmà1,scanner velocity:7.5kHz,background:24 scans,sample:24scans)at room temperature.The oil samples were positioned on a diamond crystal ATR with a single re?ection. Opus software(v.6.5,Bruker Optics,Ettlingen,Germany)was used for spectral acquisition,instrument control and preliminary?le manipulation.The spectra were compensated to eliminate disturb-ing H2O and/or CO2bands in the ratio spectra.

2.4.Data analysis

The NIR spectra were standardized by using standard normal variate(SNV)and transformed into second derivative using a the Savitzky–Golay method with cubic smoothing and seven segment size,in order to remove and minimise any unwanted spectral con-tribution(arising from e.g.light scatter).The MIR spectral data were pre-treated using multivariate scatter correction(MSC)and second derivative transform calculation(Savitzky–Golay method, gap size=7data points).

To apply classi?cation methods,the virgin olive oil samples were divided into four classes on the basis of their sensory attribute.

Classi?cation and class-modelling techniques,applied to spec-tral data,investigated were linear discriminant analysis(LDA) and soft independent modelling of class analogy(SIMCA).

LDA(Massart et al.,1998)is a probabilistic classi?cation tech-nique which searches for directions(canonical variables)with maximum separation among categories;the?rst canonical vari-able is the direction of maximum ratio between inter-class and intra-class variances,based on selecting appropriate latent vari-ables,is a supervised pattern recognition method,which seeks to?nd a linear transformation by maximizing the between-class variance and minimizing the within-class variance.SIMCA(Wold

Table1

Geographical origin of extra virgin olive oils.

Country Region/area Sample no.

Italy Calabria5

Campania5

Emilia-Romagna1

Lazio5

Liguria4

Marche7

Puglia24

Sardegna1

Sicilia26

Toscana8

Umbria2

Veneto1

Italy–total89

Spain Aragón1

Andalucía10

Spain–total11

Croatia Istria6

Slovenia Istria4

Israel Herzliya1

Turkey Izmir1

Total samples112

370N.Sinelli et al./Food Research International43(2010)369–375

&Sj?str?m,1977)was the?rst class-modelling technique used in chemometric;the central feature of this method is the application of principal component analysis(PCA)to the sample category studied.The number of signi?cant PCs is determined for each class and as many models as the number of classes are obtained by training.

The classi?cation methods applied to spectral data were car-ried out after having applied the algorithm stepwise decorrelation of the variables(SELECT)as feature selection technique(Forina, Lantieri,Casale,&Cerrato Oliveros,2007).SELECT is a feature selection technique based on the stepwise decorrelation of the variables.It generated a set of decorrelated variables ordered according to their Fisher weight.SELECT searches at each step, for the variables with the largest classi?cation weight.The algo-rithm allows the retention of the decorrelated variables or the ori-ginal ones.This identi?cation and decorrelation procedure continues until the decorrelated predictors do not have a signi?-cant Fisher weight.Decorrelation is especially important in the case of spectral data because contiguous variables are often highly inter-correlated.The LDA classi?cations rules performances was evaluated on the basis of the predictive ability,while the ability of SIMCA was archived by sensitivity and speci?city of the models.

All the classi?cation rules were evaluated using a cross-valida-tion procedure:the objects were divided into G cancellation groups.Objects were assigned to a cancellation group by their in-dex,n(row in the data matrix):the?rst object was assigned to cancellation group1,the second to group2,the g th to group G. Then the(g+1)th was assigned again to group1and so on.The model was computed G times.Each time,the objects in the corre-sponding cancellation group formed the evaluation(prediction) set.The other objects were the training set used to compute the model parameters.At the end of the procedure,each object has been(Gà1)times in the training set and once in the evaluation set.In cross-validation,with N objects,the number of cancellation groups can range from3to N.Cross-validation using a number of cancellation groups equal to the number objects(N)is generally known as a leave-one-out procedure(LOO).LOO produces,in each validation cycle,a very small change in the training set and this small perturbation has the consequence that the measure of the predictive ability can be overly optimistic.On the contrary,with a small number of cancellation groups,the resulting training sets are very different,and the measure of the predictive ability is not optimistic,possibly pessimistic(Casale,Casolino,Ferrara,&Forina, 2008).In this work the cross-validation was performed with?ve cancellation groups(5CV).

Table2

Sensory lexicon for sensory attributes and defects of virgin olive oil.

Description Reference Sensory attributes

Fruity Range of smells(dependent on variety)characteristic of oil from healthy fresh fruit,green or ripe, perceived directly and/or retronasally.Fruitiness is quali?ed as green if the range of smells is reminiscent

of green fruit and is characteristic of oil from green fruit.Fruitiness is quali?ed as ripe if the range of smells

is reminiscent of ripe fruit and is characteristic of oil from green and ripe fruit Commission Regulation(EC)no. 640/2008,July2008

Green fruity Olfactory sensation typical of oils obtained from olives that have been harvested before or during colour change COI/T.20/Doc.no.22,November 2005

Ripe fruity Olfactory sensation typical of oils obtained from olives that have been harvested when fully ripe COI/T.20/Doc.no.22,November

2005

Main defects

Fusty/muddy sediment Characteristic?avour of oil from olives that have been piled or stored in such a way as to have reached an

advanced stage of anaerobic fermentation,or of oil which has been left in contact with the sediment that

settles in underground tanks and vats and which has also undergone a process of anaerobic fermentation

Commission Regulation(EC)no.

640/2008,July2008

Musty/humid Characteristic?avour of oil from olives in which large numbers of fungi and yeasts have developed as a result of storage for several days in humid conditions Commission Regulation(EC)no. 640/2008,July2008.

Rancid Flavour of oil that has undergone an intense process of oxidation Commission Regulation(EC)no.

640/2008,July2008

Winey-vinegary/ acid-sour Characteristic?avour of certain oils reminiscent of wine or vinegar.This?avour is mainly due to the

aerobic fermentation of the olives or of olive paste left on pressing mats which have not been properly

cleaned,leading to the formation of acetic acid,ethyl acetate and ethanol

Commission Regulation(EC)no.

640/2008,July2008

Secondary defects

Brine Flavour of oil extracted from olives which have been preserved in brine Commission Regulation(EC)no.

640/2008,July2008

Cucumber Characteristic?avour of oil kept too long in hermetically sealed containers,notably in tins,attributed to formation of2,6-non-adienal Commission Regulation(EC)no. 640/2008,July2008

Earthy Flavour of oil from olives collected with earth or mud on them and not washed Commission Regulation(EC)no.

640/2008,July2008

Esparto Characteristic?avour of oil from olives pressed in new esparto mats.The?avour may vary depending on whether the mats are made of green or dried esparto Commission Regulation(EC)no. 640/2008,July2008

Greasy Flavour reminiscent of diesel,grease or mineral oil Commission Regulation(EC)no.

640/2008,July2008

Grubby Flavour of oil from olives heavily attacked by grubs of the olive?y(Bactrocera oleae)Commission Regulation(EC)no.

640/2008,July2008

Hay/wood Characteristic?avour of certain oils from dry olives Commission Regulation(EC)no.

640/2008,July2008.

Heated or burnt Characteristic?avour caused by excessive and/or prolonged heating during production,particularly by thermo-mixing of the paste in unsuitable https://www.wendangku.net/doc/af323238.html,mission Regulation(EC)no. 640/2008,July2008

Metallic Flavour reminiscent of metal,characteristic of oil that has been in prolonged contact with metallic surfaces during crushing,mixing,pressing or storage Commission Regulation(EC)No 640/2008,July2008.

Rough Thick and pasty mouthfeel produced by certain old oils Commission Regulation(EC)no.

640/2008,July2008

Vegetable water Flavour acquired by the oil as a result of prolonged contact with vegetable water which has undergone fermentation Commission Regulation(EC)no. 640/2008,July2008

Wet wood Characteristic?avour of oil extracted from olives damaged by frost while on the tree Commission Regulation(EC)no.

640/2008,July2008

N.Sinelli et al./Food Research International43(2010)369–375371

All chemometric techniques were performed by using V-PAR-VUS package (Forina et al.,2008).3.Results and discussion 3.1.Sensory analysis

The 112samples were ?rstly divided in ‘‘defective”and ‘‘not defective”by means of olfactive screening carried out by Of?cial Panel.In this ?rst step,62samples that presented unpleasant notes due to the main or secondary defects (see Table 2)have been clas-si?ed as ‘‘defective”.Then,the 50‘‘not defective”samples were classi?ed into ‘‘low”(18samples),‘‘medium”(18samples)and ‘‘high”(14samples)according to the fruity aroma intensity per-ceived by assessors.This distinction in low,medium and high fru-ity corresponded to the descriptions permitted in the label according to the last EC Regulation:light,medium and intense,respectively (EC Reg.,640/2008).As reported in literature,olive fruity attribute is due to the presence of some speci?c small mol-ecules that at the analysis temperature (around 28°C and 37°C

during olfactive and gustative phases,respectively)tend to par-tially volatilize (Angerosa,2000,2002).On the other hand,the con-centration and composition of these volatile compounds in virgin olive oils are strongly affected by agronomical and technological parameters,such as olive cultivar,area of cultivar,climate,degree of maturation,crop season and production process (Angerosa,2002;Baccouri et al.,2008).For this reasons with the aim to con-struct a robust model several real samples have been selected to introduce an elevate number of variance sources (olive cultivar,area of olive growing,technological plant for olive oil production and storage condition).

3.2.FT-NIR and FT-IR spectroscopy

Fig.1show the raw NIR (Fig.1a)and MIR (Fig.1b)spectra of vir-gin olive oil,respectively.The spectra did not evidence an obvious differences from visual inspection on the basis of their sensory attribute.In the NIR region,bands around 8252cm à1arise from 2nd overtones of C A H stretching vibrations while those at 7180and 7075cm à1are due to the combination band of C–H.The

peaks

Fig.1.FT-NIR (a)and FT-IR (b)raw spectra of extra virgin olive

oils.

Fig.2.Projection on the ?rst two canonical variables of LDA computed on reduced matrix of NIR (a)and MIR (b)samples are shown by their class symbol:D (‘defective oils’),L (‘low fruity’),M (‘medium fruity’),H (‘high

fruity’).

Fig.3.Projection on the ?rst two canonical variables of LDA computed on reduced matrix of NIR (a)and MIR (b)samples are shown by their class symbol:L (‘low fruity’),M (‘medium fruity’),H (‘high fruity’).

372N.Sinelli et al./Food Research International 43(2010)369–375

at5787and5671cmà1arise from the?rst overtone of C–H stretching vibrations of methyl,methylene and ethylene groups (Christy,Kasemsumran,Du,&Ozaki,2004;Cozzolino,Murray, Chree,&Scaife,2005).Small peaks at4659and4572cmà1are associated with combination bands of C–H and C A O stretching vibration.The MIR spectra are dominated by some peaks at2924, 2852,1743,1463,1377,1238,1163,1114,1099and721cmà1. Absorbance at2924and2852cmà1are due to bands arising from CH2stretching vibrations,asymmetric and symmetric,respec-tively.The major peak at1743cmà1arises from C@O stretching vibrations;the bands at1463and1377cmà1arise from CH2and CH3scissoring vibration,while those at1238,1163,1114, 1099cmà1are associated with the C A O stretching vibration.The peak at721cmà1corresponds to CH2rocking mode(Colthup,Daly, &Wiberly,1990;Yang,Irudayaraj,&Paradkar,2005).

To apply classi?cation methods,the virgin olive oil samples were divided into four classes on the basis of the sensory analysis:‘defective oils’(class1),‘low fruity’(class2),‘medium fruity’(class 3)and‘high fruity’(class4).

The stepwise decorrelation of the variables(SELECT)was ap-plied on the NIR and MIR matrices in order to delete no useful information.In both NIR and MIR regions the wavenumbers se-lected are associated with the absorption bands of the most impor-tant compounds responsible of the olive oil defects belonging to different chemical classes,such as aldehydes,ketones,esters,acids and alcohols(Angerosa,2000,2002;Baccouri et al.,2008;Bendini, Cerretani,Cichelli,&Lercker,2008).In particular,the principal wavenumbers selected in the NIR region are in the range7100–7000cmà1,5800–5,5810cmà1and4970–4600cmà1,where there are the absorptions bands of O–H associated with alcohol,C–H and C@O(?rst overtone and combination bands of stretching vibration)(Workman&Weyer,2008).In the mid region the main ranges selected(3000–2700,1700–1400,1200–1000cmà1)corre-spond to the absorption bands of CH3,C@O and C–O stretching vibration(Colthup et al.,1990).

LDA was then performed on the reduced data matrices.The re-sults,obtained by LDA classi?cation(Table3),show a good ability of NIR and MIR spectroscopy to classify virgin olive oils on the basis of the fruit intensity.In particular,the prediction ability obtained using the NIR data was76.3%,as average value of samples correctly classi?ed.The olive oils characterized by low(class2)and high fru-ity attribute(class4)were better classi?ed than the others.In fact, 94.4%and100%of the samples belonging to class2and to class4, respectively,were correctly classi?ed.Similar results were ob-tained by MIR spectroscopy;the percentages of correct classi?ca-tion and prediction were99.7%and82.3%,respectively.

A graphical display of these results is shown in Fig.2.In partic-ular,Fig.2a,obtained by the projection on the?rst two canonical variables of LDA computed on reduced matrix of NIR data,shows a good separation of‘low fruity’and‘high fruity’oils.On the con-trary,some overlap was observed between‘medium fruity’and ‘defective oils’.The same behaviour was achieved for MIR data (Fig.2b).Being the class1samples classi?ed on the basis of defects and not on their fruity intensity,a partial overlapping of oils belonging to1and3classes may be justi?ed.

In order to classify the extra virgin olive oils as‘defective’oils,a LDA classi?cation was built considering only two categories:one class as‘defective’oils and the other one as‘not defective’oils. The results are shown in Table4.The high percentages of correct classi?cation and prediction archived in both near and mid region show a good ability of these techniques for the identi?cation of a ‘defective’olive.

Moreover in order to classify the samples on the basis of their fruity intensity,the data processing was performed using only the‘‘not defective oils”,which were grouped into three classes:‘low fruity’(class1),‘medium fruity’(class2)and‘high fruity’(class3).The spectral data of these samples were sub-jected to analysis by LDA and SIMCA,developing models for the three different extra virgin olive oils.Also in this case the results,were validated using?ve cancellation groups(5CV).Ta-ble5shows the results obtained by LDA classi?cation.Very good results were achieved with both NIR and MIR spectros-copy,in fact the percentages of correct prediction were98%, and100%respectively.Fig.3shows projection of the samples on the?rst two canonical variables of LDA and highlights a good ability of NIR(Fig.3a)and MIR(Fig.3b)techniques to classify extra virgin olive oils on the basis of the fruity intensity aroma.

Finally,both NIR and MIR reduced data matrices,were sub-jected to analysis by SIMCA,developing models for the three differ-ent oils(low fruity,medium fruity and high fruity).

Internal prediction rate,sensitivity(the non-error rate for each category)and speci?city(the percentage of objects of the other class rejected by the class model under study)are shown in Ta-ble6.The results obtained by SIMCA were lower than those achieved by LDA method.However models built using SIMCA showed,as average value,a very high sensitivity(98%for both NIR and MIR spectroscopy)and a good speci?city(89.2%for NIR spectroscopy and71.6%for MIR spectroscopy),highlighting a good correlation between spectral pro?le and the sensory attributes of the oils.

Table3

LDA results after feature selection.Cross-validation performed with?ve cancellation groups.

%Samples correctly classi?ed (class1,defective)%Samples correctly classi?ed

(class2,low fruity)

%Samples correctly classi?ed

(class3,medium fruity)

%Samples correctly classi?ed

(class4,high fruity)

Average

NIR

Calibration91.698.998.910094.9 Prediction67.294.472.210076.3

MIR

Calibration99.310010010099.7 Prediction83.976.788.976.682.3

Table4

LDA results after feature selection performed with two class:‘defective’oils and‘not

defective’oils.

%Samples correctly classi?ed(class1, defective)%Samples correctly

classi?ed(class2,not

defective)

Average

NIR

Calibration100100100

Prediction98.410099.1

MIR

Calibration100100100

Prediction98.49898.2

N.Sinelli et al./Food Research International43(2010)369–375373

4.Conclusions

This study demonstrates the potential of NIR and MIR spectros-copy,coupled with chemometric analysis,to classify extra virgin olive oil on the basis of their fruity aroma intensity.Good classi?-cation models were achieved after feature selection,necessary to delete wavenumbers associated to non-useful information.Best re-sults were obtain using only the‘‘not defective oils”,which were grouped into three classes:‘low fruity’(class1),‘medium fruity’(class2)and‘high fruity’(class3).The results showed that the IR spectroscopy is a reliable,cheap and fast classi?cation tool able to draw a complete?ngerprint of a food product,describing its intrinsic quality attributes,that include its sensory attributes.A next step of this work will be the study of the suitability of this method to determine the intensity of bitter and pungent attributes. In fact,this fast and economical tool could be applied for on-line monitoring of extra virgin olive oil during bottling according to re-cent law for virgin olive oils labeling(EC Reg.,1019/2002and EC Reg.,640/2008).

Acknowledgements

The authors gratefully acknowledge Fiera di Trieste and Teatro Naturale who kindly allowed us to use the samples that have been submitted to sensory analysis for the Olio Capitale international ol-ive oil award.

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Table5

LDA results after feature selection using three classes(low fruity,medium fruity,and high fruity).Cross-validation performed with?ve cancellation groups.

%Samples correctly classi?ed (class1,low fruity)%Samples correctly classi?ed

(class2,medium fruity)

%Samples correctly classi?ed

(class3,high fruity)

Average

NIR

Calibration100100100100 Prediction10010092.998.0

MIR

Calibration100100100100 Prediction100100100100

Table6

SIMCA results after feature selection using three classes(low fruity,medium fruity,high fruity).Cross-validation performed with?ve cancellation groups.

%Samples correctly classi?ed (class1,low fruity)%Samples correctly classi?ed

(class2,medium fruity)

%Samples correctly classi?ed

(class3,high fruity)

Mean sensitivity(%)Mean speci?city(%)

NIR

Calibration94.794.41009889.6 Prediction73.772.264.3

MIR

Calibration88.974.71009871.6 Prediction66.763.171.4

374N.Sinelli et al./Food Research International43(2010)369–375

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有机物红外光谱的测绘及结构分析

有机物红外光谱的测绘及结构分析 一、实验目的 1、掌握液膜法制备液体样品的方法； 2、掌握溴化钾压片法制备固体样品的方法； 3、学习并掌握IR-408型和IR-435型红外光谱仪的使用方法； 4、初步学会对红外吸收光谱图的解析。 二、方法原理 物质分子中的各种不同基团，在有选择地吸收不同频率的红外辐射后，发生振动能级之间的跃迁，形成各自独特的红外吸收光谱。据此可对物质进行定性、定量分析。特别是对化合物结构的鉴定，应用更为广泛。 基团的振动频率和吸收强度与组成基团的原子质量、化学键类型及分子的几何构型等有关。因此根据红外吸收光谱的峰位置、峰强度、峰形状和峰的数目，可以判断物质中可能存在的某些官能团，进而推断未知物的结构。如果分子比较复杂，还需结合紫外光谱、核磁共振谱以及质谱等手段作综合判断。最后可通过与未知样品相同测定条件下得到的标准样品的谱图或已发表的标准谱图（如Sadtler 红外光谱图等）进行比较分析，做出进一步的证实。如找不到标准样品或标准谱图，则可根据所推测的某些官能团，用制备模型化合物的方法来核实。 乙酰乙酸乙酯有酮式及烯醇式互变异构： 在红外光谱上能够看出各异构体的吸收带。 三、试剂和仪器 仪器 Equinox 55型傅立叶变换红外光谱仪（德国Bruker 公司）或IR-408型红外分光光度计（日本岛津公司）；可拆式液池；压片机；玛瑙研钵；氯化钠盐片；标准聚苯乙烯薄膜；快速红外干燥箱。 试剂 苯甲酸：于80℃下干燥24h ，存于保干器中；溴化钾：于130℃下干燥24h ，存于保干器中；无水乙醇、苯胺、乙酰乙酸乙酯、四氯化碳 四、实验内容 1、波数检验：将聚苯乙烯薄膜插入IR-408型红外光谱仪的样品池处，从4000-650cm -1进行波数扫描，得到吸收光谱。 2、测绘无水乙醇、苯胺、乙酰乙酸乙酯的红外吸收光谱——液膜法：戴上指套，取两片氯化钠盐片，用四氯化碳清洗其表面，并放入红外灯下烘干备用。在可拆式液体池的金属池板上垫上橡胶圈，在孔中央位置放一盐片，然后滴半滴液体试样于盐片上，将另一盐片平压在上面（注意不能有气泡） ，垫上橡胶圈，将另一金属片盖上，对角方向旋紧螺丝（螺丝

橄榄油的等级分类

橄榄油的等级分类 不同的产区、橄榄树品种的橄榄果，出产不同标准的橄榄油，据 2009年10月份出台的《GB 23347-2009》橄 榄油、油橄榄果渣油国家标准，除了酸度之 外，反式脂肪酸也是辨别精炼橄榄油和特级 初榨橄榄油的标准：即c18:1T、c18:2T+c1 8:3T综合值≤0.1%。根据国际橄榄油理事会 标准，所谓橄榄果渣油即油橄榄果渣油不能 标明橄榄油名称。 国际橄榄油协会将橄榄油分为初榨橄榄油（Virgin Olive Oil）和精炼橄榄油（Lampante Olive Oil或Refined Olive Oil）两大类。 初榨橄榄油或称为天然橄榄油，是直接从新鲜的橄榄果实中采取机械冷榨、经过过滤等处理除去异物后得到的油脂。 1.单果特级初榨橄榄 油（Single Variety Extr a Virgin）,橄榄油之王， 品质最高的橄榄油，从单 一橄榄品种的新鲜果实中 榨取的第一道冷榨油，口 感香味可辨识，酸度0.1- 0.6。

2.特级初榨橄榄油（Extra Virgin）：是最高级别、质量最高的橄榄油，是纯天然产品。口味绝佳，有淡雅怡人的植物芬芳，酸度不超过1%。 3.优质初榨橄榄油（Fine Virgin）：酸度稍高，但不超过2%，味道纯正、芳香。 4.普通初榨橄榄油（Ordinary Virgin）：口味与风味尚可，酸度不超过3.3% 。 5.精炼橄榄油：是指酸度超过3.3%的初榨橄榄油精炼后所得到的橄榄油，或成为“二次油”。精炼橄榄油可分为两个级别： 6.普通橄榄油（Olive Oil）：精炼橄榄油与一定比例的初榨橄榄油混合，以调和味道与颜色，其酸度在1.5%以下，呈透明的淡金黄色。精炼橄榄杂质油（Refined Olive-Pomace Oil）：是通过溶解法从油渣中提取并经过精炼而得到的橄榄油。

人工智能与模式识别

人工智能与模式识别 摘要：信息技术的飞速发展使得人工智能的应用围变得越来越广，而模式识别作为其中的一个重要方面，一直是人工智能研究的重要方向。在介绍人工智能和模式识别的相关知识的同时，对人工智能在模式识别中的应用进行了一定的论述。模式识别是人类的一项基本智能，着20世纪40年代计算机的出现以及50年代人工智能的兴起，模式识别技术有了长足的发展。模式识别与统计学、心理学、语言学、计算机科学、生物学、控制论等都有关系。它与人工智能、图像处理的研究有交叉关系。模式识别的发展潜力巨大。 关键词：模式识别；数字识别；人脸识别中图分类号； Abstract: The rapid development of information technology makes the application of artificial intelligence become more and more widely. Pattern recognition, as one of the important aspects, has always been an important direction of artificial intelligence research. In the introduction of artificial intelligence and pattern recognition related knowledge at the same time, artificial intelligence in pattern recognition applications were discussed.Pattern recognition is a basic human intelligence, the emergence of the 20th century, 40 years of computer and the rise of artificial intelligence in the 1950s, pattern recognition technology has made great progress. Pattern recognition and statistics, psychology,

橄榄油压榨过程

油橄榄果实直接食用的不多，大多用来榨油。制作橄榄油必须在油橄榄完全成熟之前采收，因为过熟的橄榄容易在运送中途发酵氧化影响橄榄油的质量。因为油橄榄果实表皮很易损伤而发酵，所以在大部分地区主要靠手工采摘。同样为了减少发酵，油橄榄果必须在采摘后24小时内进行加工。清洗烘干后的油橄榄先压磨成糊状，再把这些糊泥铺在以椰子壳纤维做的圆形中空垫子上，把这些糊泥垫子层层堆栈，套放在中心有大钢柱的机器内，略加压后油就流出来了全过程必须在摄氏30度以内的温度下进行。这种油就是所谓初榨橄榄油（Virgin Olive Oil），加工过程中完全不经过化学处理，适用于直接食用或做凉拌菜用。初榨橄榄油的副产品油橄榄渣使用化学浸出法获得的橄榄油被称为精炼橄榄油（Lampante Olive Oil或Refined Olive Oil），一般不可直接食用，可用作烹饪。从前还有用加水法抽取橄榄油。在橄榄糊泥上淋热水，等到流出的汁液凉了以后，油质会浮在水面上，再把油和水分离，就得到橄榄油了。刚制好的橄榄油必须放置在黑暗干爽的地方贮存熟成一个月后才装瓶贩卖。一般市面上卖的橄榄油，都已经过过滤手续。未经过滤的橄榄油则较浑浊、色深，但是味道比较强烈香浓。有越来越多的当地人，喜欢食用没有过滤的橄榄油。油橄榄有许多品种，橄榄油彼此味道也大不相同。除此之外，自然环境以及采收等也是影响橄榄油味道的因素。 刚榨出的橄榄油 根据液体中的酸性值得不同初榨橄榄油又分为3个级别：特级初榨橄榄油（Extra Virgin），特级初榨橄榄油的过氧化值须低于10%，酸度介于0.3至0.8%之间。色泽金黄色中略带有绿色，色泽清澈透明，味道有橄榄果香、苦、辣味。如果橄榄油不含辣的味道，则说明它是没有抗氧化剂的，而且苦味越重，则对身体健康越好。优质初榨橄榄油（Fine Virgin），酸性值不超过2%。普通初榨橄榄油（Ordinary Virgin）酸性值不超过3.3%。一般橄榄油的色泽按等级高低从到青绿色到深黄色，但不是颜色越深越好。

黄庆明 模式识别与机器学习 第三章 作业

·在一个10类的模式识别问题中，有3类单独满足多类情况1，其余的类别满足多类情况2。问该模式识别问题所需判别函数的最少数目是多少？ 应该是252142 6 *74132 7=+=+ =++C 其中加一是分别3类 和 7类 ·一个三类问题，其判别函数如下： d1(x)=-x1, d2(x)=x1+x2-1, d3(x)=x1-x2-1 (1)设这些函数是在多类情况1条件下确定的，绘出其判别界面和每一个模式类别的区域。 (2)设为多类情况2，并使：d12(x)= d1(x), d13(x)= d2(x), d23(x)= d3(x)。绘出其判别界面和多类情况2的区域。

(3)设d1(x), d2(x)和d3(x)是在多类情况3的条件下确定的，绘出其判别界面和每类的区域。 ·两类模式，每类包括5个3维不同的模式，且良好分布。如果它们是线性可分的，问权向量至少需要几个系数分量？假如要建立二次的多项式判别函数，又至少需要几个系数分量？（设模式的良好分布不因模式变化而改变。） 如果线性可分，则4个 建立二次的多项式判别函数，则102 5 C 个 ·(1)用感知器算法求下列模式分类的解向量w: ω1: {(0 0 0)T , (1 0 0)T , (1 0 1)T , (1 1 0)T } ω2: {(0 0 1)T , (0 1 1)T , (0 1 0)T , (1 1 1)T } 将属于ω2的训练样本乘以（-1），并写成增广向量的形式。 x ①=(0 0 0 1)T , x ②=(1 0 0 1)T , x ③=(1 0 1 1)T , x ④=(1 1 0 1)T x ⑤=(0 0 -1 -1)T , x ⑥=(0 -1 -1 -1)T , x ⑦=(0 -1 0 -1)T , x ⑧=(-1 -1 -1 -1)T 第一轮迭代：取C=1，w(1)=(0 0 0 0) T 因w T (1) x ① =(0 0 0 0)(0 0 0 1) T =0 ≯0，故w(2)=w(1)+ x ① =(0 0 0 1) 因w T (2) x ② =(0 0 0 1)(1 0 0 1) T =1>0，故w(3)=w(2)=(0 0 0 1)T 因w T (3)x ③=(0 0 0 1)(1 0 1 1)T =1>0，故w(4)=w(3) =(0 0 0 1)T 因w T (4)x ④=(0 0 0 1)(1 1 0 1)T =1>0，故w(5)=w(4)=(0 0 0 1)T 因w T (5)x ⑤=(0 0 0 1)(0 0 -1 -1)T =-1≯0，故w(6)=w(5)+ x ⑤=(0 0 -1 0)T 因w T (6)x ⑥=(0 0 -1 0)(0 -1 -1 -1)T =1>0，故w(7)=w(6)=(0 0 -1 0)T 因w T (7)x ⑦=(0 0 -1 0)(0 -1 0 -1)T =0≯0，故w(8)=w(7)+ x ⑦=(0 -1 -1 -1)T 因w T (8)x ⑧=(0 -1 -1 -1)(-1 -1 -1 -1)T =3>0，故w(9)=w(8) =(0 -1 -1 -1)T 因为只有对全部模式都能正确判别的权向量才是正确的解，因此需进行第二轮迭代。 第二轮迭代： 因w T (9)x ①=(0 -1 -1 -1)(0 0 0 1)T =-1≯0，故w(10)=w(9)+ x ① =(0 -1 -1 0)T

特级初榨橄榄油

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目录 摘要................................................................................... I ABSTRACT......................................................................... II 1 傅里叶红外光谱仪的发展历史 (1) 2 基本原理 (4) 2.1光学系统及工作原理 (4) 2.2傅立叶变换红外光谱测定 (6) 2.3傅立叶变换红外光谱仪的主要特点 (7) 3 样品处理 (8) 3.1气体样品 (8) 3.2液体和溶液样品 (8) 3.3固体样品 (8) 4 傅立叶变换红外光谱仪的应用 (9) 4.1在临床医学和药学方面的应用⑷ (9) 4.2在化学、化工方面的应用 (10) 4.3在环境分析中的应用 (11) 4.4在半导体和超导材料等方面的应用⑼ (11) 5 全文总结 (12) 参考文献 (13)

橄榄油识别

橄榄油识别 于我国对进口橄榄油的等级译名没有统一的要求，所以有些进口商将橄榄油翻译译名作了混淆，使得消费者很多时候是雾里看花。但是不论译名如何，消费者只要留意橄榄油瓶正标的外文等级标识就好了。这些标识只适用于从欧盟进口的橄榄油，因为欧盟对橄榄油等级的划分很严格，并对每批欧盟出口的橄榄油都有港口检验要求。所以，中国从欧盟所属成员国进口的橄榄油，对上边标注的等级还是完全可以放心的。下面介绍几种橄榄油的标识和译名：有些橄榄油瓶上标识文字如下：orujo(西班牙文),sansa（意大利文），pomace（英文）。这些标志都是果渣油的标志，所以不管经销商怎么宣称有这种标识的橄榄油有多高贵，他们只是橄榄果渣油。这里，我们并不是说橄榄果渣油不好，实际上由于初榨橄榄油产量比较少，价格昂贵，很多欧洲家庭用橄榄果渣油进行加热烹调。只是渣油的价格比特级初榨的橄榄油要便宜大约20%左右。3%初榨油与97%的精炼橄榄油勾兑叫：Very Mild Flavour，这就是Extra Light,这种油实际上只勾兑了很少的初榨橄榄油。由于初榨橄榄油的价值比较高，所以，只勾兑了3%的初榨橄榄油的精炼油实际上只是带了一点点橄榄油香。有些橄榄油瓶上标识文字如下：只标识Olive Oil(英文),aceite de Oliva（西班牙文），或者在标识中出现这几个字：Refined（英文），Refinado（西班牙文），这些是精制或精炼橄榄油的意思，有些人也翻译成纯正橄榄油，100%橄榄油，他们其实都是精炼橄榄油。有些橄榄油瓶上的标识文字如下：Extra Light Oilve Oil，其实这不是初榨橄榄油，它字面上的意思叫做超轻橄榄油，它也是一种精炼橄榄油，我们上文说过精炼橄榄油在出厂时会勾兑初榨橄榄油来增加味道，颜色。而根据添加不同比例的初榨橄榄油会有不同的名称：50%初榨橄榄油与50%的精炼橄榄油勾兑叫：More Flavour （更好的香味）30%初榨橄榄油与70%的精炼橄榄油勾兑叫：Medium Flavour （中等香味）15%初榨橄榄油与85%的精炼橄榄油勾兑叫：Mild Flavour （淡味）我们说了这么多辨识橄榄油的办法，其实也很简单，正标上没有Virgin（英文）、Virgen （西班牙文）字样的，都不是初榨橄榄油。无论精炼橄榄油和橄榄果渣油，他们都是经过精炼的橄榄油。以上介绍的只是欧盟橄榄油的不同叫法的一部分，这只是适用于欧盟出口的，在欧盟境内灌装好的橄榄油，不适用于除欧盟以外，如土耳其，突尼斯等其他国家的橄榄油标识规范，以及从国外进口在国内分装的橄榄油的叫法。另外，区别原装进口与国内分装的办法也很简单，只要看看橄榄油瓶上的国际条形码缀，690/693 开头的都是中国产的商品。而西班牙的条形码是84，法国是30/37条形码是一个商品的标志，有着原产国信息。所以消费者很简单的就可以区分出每种橄榄油是否为原装进口。在这里要指出，也有厂商国内灌装的，注册的条码是国外的。所以消费者有时候还需要其他手段来辨别。 根据国际橄榄油理事会(IOOC)的标准，纯正的特级原生橄榄油（Extra Virgin Olive Oil）除了生化指标需符合标准外，感官指标也需要符合要求（例如辣味，）且无瑕疵。达不到标准的橄榄油营养成份和微量元素含量有限，不能作为特级原生橄榄油销售。目前，橄榄油在中国处于起步和发展期，呼吁消费者购买前尽量掌握一些橄榄油鉴别的方法，及识别虚假宣传的能力。如何鉴别不同等级的橄榄油呢～橄榄油（Olive oil）是指纯粹从橄榄果中通过冷榨方式并排除用溶剂浸提或重脂化过程获取的成品油，而且不得掺杂其他油类。任何情况下，橄榄果渣油都不能使用“橄榄油”这一名称。 <一> 从橄榄油包装上的外文和图标可以准确识别橄榄油的品质： 1、用纯粹物理方法冷榨出来的油-----特级初榨橄榄油EXTRA VIRGIN OLIVE OIL（英文）ACEITI DE OLIV A VIRGEN EXTRA（西班牙文） 2、用化学方法精炼并加一定比例的初榨橄榄油的油----纯正橄榄油PURE OLIVE OIL （英文）ACEITE DE OLIV A PURO（西班牙文） 3、不能称其为橄榄油的油-----果渣橄榄油POMACEOLIVE OIL（英文）ACEITE

橄榄油的知识-简参考资料

对橄榄油的知识、认识、了解、保存、用途 1、橄榄油是什么？用来做什么的？ 答：橄榄油是用成熟的油橄榄鲜果直接冷榨制取的油脂。油橄榄树是地中海的一种优质木本油料树种，已被世界上多个国家引种，我国于1964年首次引进树苗并栽种成活。世界卫生组织的调查结果表明：以橄榄油为食用油的希腊克里特岛居民，心血管系统疾病、癌症、老年痴呆症的发病率极低。巴黎首届人类营养大会报道：美国冠心病发病率高于法国两倍。心脏病在美国的发病率占各种疾病发病率的３２％，而意大利只占１２％，希腊仅占８％。究其原因，这与希腊当地居民长期食用橄榄油有密切关系。 人体所需的脂肪从植物或动物中获取，其主要成分是脂肪酸。脂肪有饱和与不饱和之分，不饱和脂肪酸又称为必需脂肪酸。橄榄油与其它植物食用油相比，含有丰富的不饱和脂肪酸，易被人体消化吸收，又不易氧化沉积在人体血管壁、心脏冠状动脉等部位。它是9类96种物质的混合物，正是这些成分使其在医学和营养上发挥了重要作用而成为理想的食用油。那么，橄榄油具体对人体有哪些益处呢？ 首先，能增进消化系统功能。橄榄油中含有比任何植物油都要高的单不饱和脂肪酸，另外还富含丰富的脂溶性维生素A、D、E、F、K和胡萝卜素等及天然抗氧化物等多种成分，不含胆固醇，因而人体消化吸收率极高。它有减少胃酸、阻止发生胃炎及十二指肠溃疡等病的功能；并可刺激胆汁分泌，激化胰酶的活力；使油脂降解，被肠黏膜吸收，以减少胆囊炎和胆结石的发生。 其次，能减少心血管疾病发生。动脉粥样硬化是现代城市中发病率较高的疾病。医学证明，这与食用过多热量及过多的动物脂肪引起的血浆中胆固醇浓度升高有关。橄榄油中的油酸还能提高高密度脂蛋白（好的胆固醇），降低低密度脂蛋白（坏的胆固醇）以保证体内对胆固醇的健康需求量。

红外光谱仪的发展

最佳答案 在过去的50多年里，近红外光谱仪经历了如下几个发展阶段： ★第一台近红外光谱仪的分光系统（50年代后期）是滤光片分光系统，测量样品必须预先干燥，使其水分含量小于15%，然后样品经磨碎，使其粒径小于1毫米，并装样品池。此类仪器只能在单一或少数几个波长下测定（非连续波长），灵活性差，而且波长稳定性、重现性差，如样品的基体发生变化，往往会引起较大的测量误差！“滤光片”被称为第一代分光技术。 ★70年代中期至80年代，光栅扫描分光系统开始应用，但存在以下不足：扫描速度慢、波长重现性差，内部移动部件多。此类仪器最大的弱点是光栅或反光镜的机械轴长时间连续使用容易磨损，影响波长的精度和重现性，不适合作为过程分析仪器使用。“光栅”被称为第二代分光技术。 ★80年代中后期至90年代中前期，应用“傅立叶变换”分光系统，但是由于干涉计中动镜的存在，仪器的在线可靠性受到限制，特别是对仪器的使用和放置环境有严格要求，比如室温、湿度、杂散光、震动等。“傅立叶变换”被称为第三代分光技术。 ★90年代中期，开始有了应用二极管阵列技术的近红外光谱仪，这种近红外光谱仪采用固定光栅扫描方式，仪器的波长范围和分辨率有限，波长通常不超过1750nm。由于该波段检测到的主要是样品的三级和四级倍频，样品的摩尔吸收系数较低，因而需要的光程往往较长。“二极管阵列”被称为第四代分光技术。 ★90年代末，来自航天技术的“声光可调滤光器”（缩写为AOTF）技术的问世，被认为是“90年代近红外光谱仪最突出的进展”，AOTF是利用超声波与特定的晶体作用而产生分光的光电器件，与通常的单色器相比，采用声光调制即通过超声射频的变化实现光谱扫描，光学系统无移动性部件，波长切换快、重现性好，程序化的波长控制使得这种仪器的应用具有更大的灵活性，尤其是外部防尘和内置的温、湿度集成控制装置，大大提高了仪器的环境适应性，加之全固态集成设计产生优异的避震性能，使其近年来在工业在线和现场（室外）分析中得到越来越广泛的应用。 非制冷红外技术发展现状（上） 尤海平(2005.11.17)

模式识别练习题(简答和计算)..

1、试说明Mahalanobis 距离平方的定义，到某点的Mahalanobis 距离平方为常数的轨迹的几何意义，它与欧氏距离的区别与联系。 答：Mahalanobis 距离的平方定义为：∑---=1 2)()(),(u x u x u x r T 其中x ，u 为两个数据，1-∑是一个正定对称矩阵（一般为协方差矩阵）。根据定义，距 某一点的Mahalanobis 距离相等点的轨迹是超椭球，如果是单位矩阵Σ，则Mahalanobis 距离就是通常的欧氏距离。 2、试说明用监督学习与非监督学习两种方法对道路图像中道路区域的划分的基本做法，以说明这两种学习方法的定义与它们间的区别。 答：监督学习方法用来对数据实现分类，分类规则通过训练获得。该训练集由带分类号的数据集组成，因此监督学习方法的训练过程是离线的。 非监督学习方法不需要单独的离线训练过程，也没有带分类号（标号）的训练数据集，一般用来对数据集进行分析，如聚类，确定其分布的主分量等。 就道路图像的分割而言，监督学习方法则先在训练用图像中获取道路象素与非道路象素集，进行分类器设计，然后用所设计的分类器对道路图像进行分割。 使用非监督学习方法，则依据道路路面象素与非道路象素之间的聚类分析进行聚类运算，以实现道路图像的分割。 3、已知一组数据的协方差矩阵为??? ? ??12/12/11，试问 (1) 协方差矩阵中各元素的含义。 (2) 求该数组的两个主分量。 (3) 主分量分析或称K-L 变换，它的最佳准则是什么？ (4) 为什么说经主分量分析后，消除了各分量之间的相关性。

答：协方差矩阵为??? ? ??12/12/11，则 (1) 对角元素是各分量的方差，非对角元素是各分量之间的协方差。 (2) 主分量，通过求协方差矩阵的特征值，用???? ? ? ?? ----121211λλ＝0得4/1)1(2=-λ，则 ?? ?=2/32/1λ，相应地：2/3=λ，对应特征向量为???? ??11，21 =λ，对应??? ? ??-11。 这两个特征向量，即为主分量。 (3) K-L 变换的最佳准则为： 对一组数据进行按一组正交基分解，在只取相同数量分量的条件下，以均方误差计算截尾误差最小。 (4) 在经主分量分解后，协方差矩阵成为对角矩阵，因而各主分量间相关性消除。 4、试说明以下问题求解是基于监督学习或是非监督学习： (1) 求数据集的主分量 (2) 汉字识别 (3) 自组织特征映射 (4) CT 图像的分割 答：(1) 求数据集的主分量是非监督学习方法； (2) 汉字识别：对待识别字符加上相应类别号—有监督学习方法； (3) 自组织特征映射—将高维数组按保留近似度向低维映射—非监督学习； (4) CT 图像分割—按数据自然分布聚类—非监督学习方法； 5、试列举线性分类器中最著名的三种最佳准则以及它们各自的原理。

高端水果种类及储存方法 (1)

1、泰国龙功果：又名龙宫果、香果、佛头果，一种生长在泰国南部湿热地区的水果。龙宫成串生长，小圆球状，果皮淡黄。外形似微型马铃薯，果肉似微型山竹，气味与龙眼相似剥皮即食，果肉浊白，味道醇美，甜中微酸，口感有点像荔枝，但果肉更有质感一些，水分更多，酸甜可口，据说是泰国王妃最爱的水果。储存方式：保鲜要求比较高，常温下保存2天左右，放冰箱可多保存3-4天，但放进冰箱之后再拿到常温环境，会加速变坏。常用运输方式：空运一次日达。 2、榴莲蜜： 榴莲蜜的果形、外皮及植株等特性均与波罗蜜相似，果形则较波罗蜜为小 (0.7 ~ 4.9 Kg/果)，皮较薄，因此又名小波罗蜜，又因其带有似榴莲般的气味，故又名为榴莲蜜。 储存方式：冰箱存放。 3、新西兰斐济果： 又名菠萝番石榴，带有较浓的清香味道、瓤白色、稍有粒状口感（有时几乎感觉不到），甘甜多汁，无比芳香，有菠萝和草莓的混合香味。 存储方式：放在冰箱里，可贮存半个月左右而不损失口感。 4、车厘子：产于美国、加拿大、智利等美洲国家的个大皮厚的进口樱桃。 存储方式：没有清洗过的车厘子，可以用塑胶袋包起，冷藏于冰箱约可以保存7天。0℃-4℃冷藏。 5、牛油果：牛油果的果实是一种营养价值很高的水果，含多种维生素、丰富的脂肪酸和蛋白质和高含量的钠、钾、镁、钙等元素，营养价值可与奶油媲美，甚至有“森林奶油”的美称，一般作为生果食用，也可被制作为菜肴和罐头。 存储方式：阴凉处或者冰箱冷藏室里，冷藏温度4~8℃最佳，不能低于4℃以免冻伤。根据成熟度不同冷藏时间一般为5~10天。 6、莲雾：又名洋蒲桃、紫蒲桃、水蒲桃、水石榴、天桃、辇雾、爪哇浦桃、琏雾，桃金娘科，原产印度、马来西亚，尤以爪哇栽培的最为著名，故又有"爪哇蒲桃"之称。 存储方式：一定要是没洗过的，必须先用报纸将莲雾包裹起来，同时放入塑料袋中，最后再将包好的莲雾放入冰箱冷藏室中。一般来说莲雾正常情况下保存时间为1周左右，冰箱保鲜可以延长至10天左右。 7、红毛丹：又名：毛荔枝，韶子，红毛果，红毛胆；为东南亚原产之无患子科大型热带果树，马来文称之“rambutan”，意为“毛茸茸之物”。

色散型红外光谱仪的原理精编版

色散型红外光谱仪的原 理精编版 MQS system office room 【MQS16H-TTMS2A-MQSS8Q8-MQSH16898】

色散型红外光谱仪的原理可用图5—12说明之。从光源发出的红外辐射，分成二束，一束通过试样他，另一束通过参比他，然后进入单色器。在单色器内先通过以一定频率转动的扇形镜(斩光器)，其作用与其它的双光束光度计一样，是周期地切割二束光，使试样光束和参比光束交替地进入单色器中的色散棱镜或光栅，最后进人检测器。随着扇形镜的转动，检测器就交替地接受这二束光。 假定从单色器发出的为某波数的单色光，而该单色光不被试样吸收，此时二束光的强度相等，检测器不产生交流信号；改变波数，若试样对该波数的光产生吸收，则二束光的强度有差异，此时就在检测器上产生一定频率的交流信号(其频率决定于斩光器的转动频率)。通过交流放大器放大，此信号即可通过伺服系统驱动参比光路上的光楔(光学衰减器)进行补偿，此时减弱参比光路的光强，使投射在检测器上的光强等于试样光路的光强。试样对某一波数的红外光吸收越多，光楔也就越多地遮住参比光路以使参比光强同样程度地减弱，使二束光重新处于平衡。试样对各种不同波数的红外辐射的吸收有多有少，参比光路上的光楔也相应地按比例移动以进行补偿。记录笔与光楔同步，因而光楔部位的改变相当于试样的透射比，它作为纵坐标直接被描绘在记录纸上。由于单色器内棱镜或光栅的转动，使单色光的波数连续地发生改变，并与记录纸的移动同步，这就是横坐标。这样在记录纸上就描绘出透射比T对波数(或波长)的红外光谱吸收曲线。 上例是双光束光学自动平衡系统的原理。也有采用双光束电学自动平衡系统来进行工作的仪器。这时不是采用光楔来使两束光达到平衡，而是测量两个电信号的比率。 由上述可见，红外光谱仪与紫外—可见分光光度计类似，也是由光源、单色器、吸收池、检测器和记录系统等部分所组成。但由于红外光谱仪与紫外—可见分光光度计工作的波段范围不同，因此，光源、透光材料及检测器等都有很大的差异。现将中红外光谱仪的主要部件简要介绍如下。 1．光源 红外光谱仪中所用的光源通常是一种惰性固体，用电加热使之发射高强度连续红外辐射。常用的有能斯特灯和硅碳棒两种。 能斯特灯(Nernstglower)是由氧化锆、氧化钇和氧化钍烧结制成，是一直径为l~3mm，长约20~50mm的中空棒或实心棒，两端绕有铂丝作为导线。在室温下，它是非导体，但加热至800℃时就成为导体并具有负的电阻特性，因此，在工作之前，要由一辅助加热器进行预热。这种光源的优点是发出的光强度高，使用寿命可达6个月至一年，但机械强度差，稍受压或受扭就会损坏，经常开关也会缩短其寿命。

模式识别报告 bayes分类

西安交通大学 《模式识别》实验一——IRIS正态分布假设下的贝叶斯分类 吴娟梅硕2081 3112313030

对于具有多个特征参数的样本（如本实验的iris 数据样本有4d =个参数），其正态分布的概率密度函数可定义为 112 2 11()exp ()()2(2)T d p π-??= --∑-???? ∑ x x μx μ 式中，12,,,d x x x ????=x 是d 维行向量，12,,,d μμμ????=μ 是d 维行向量，∑是d d ?维协方差矩阵，1-∑是∑的逆矩阵，∑是∑的行列式。 本实验我们采用最小错误率的贝叶斯决策，使用如下的函数作为判别函数 ()(|)(), 1,2,3i i i g p P i ωω==x x （3个类别） 其中()i P ω为类别i ω发生的先验概率，(|)i p ωx 为类别i ω的类条件概率密度函数。 由其判决规则，如果使()()i j g g >x x 对一切j i ≠成立，则将x 归为i ω类。 我们根据假设：类别i ω，i=1,2,……,N 的类条件概率密度函数(|)i p ωx ，i=1,2,……,N 服从正态分布，即有(|)i p ωx ~(,)i i N ∑μ，那么上式就可以写为 112 2 ()1()exp ()(),1,2,32(2)T i i d P g i ωπ-?? = -∑=???? ∑ x x -μx -μ 对上式右端取对数，可得 111()()()ln ()ln ln(2)222 T i i i i d g P ωπ-=-∑+-∑-i i x x -μx -μ 上式中的第二项与样本所属类别无关，将其从判别函数中消去，不会改变分类结果。则判别函数()i g x 可简化为以下形式 111 ()()()ln ()ln 22 T i i i i g P ω-=-∑+-∑i i x x -μx -μ