Multi-block classification of Italian semolina based on Near Infrared Spectroscopy (NIR) analysis and alveographic indices

Journal Pre-proofs Multi-block classification of Italian semolina based on Near Infrared Spectroscopy (NIR) analysis and alveographic indices Patrizia Firmani, Alessandro Nardecchia, Francesca Nocente, Laura Gazza, Federico Marini, Alessandra Biancolillo PII: DOI: Reference:

S0308-8146(19)31804-7 https://doi.org/10.1016/j.foodchem.2019.125677 FOCH 125677

To appear in:

Food Chemistry

Received Date: Revised Date: Accepted Date:

15 June 2019 29 August 2019 7 October 2019

Please cite this article as: Firmani, P., Nardecchia, A., Nocente, F., Gazza, L., Marini, F., Biancolillo, A., Multiblock classification of Italian semolina based on Near Infrared Spectroscopy (NIR) analysis and alveographic indices, Food Chemistry (2019), doi: https://doi.org/10.1016/j.foodchem.2019.125677

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Multi-block classification of Italian semolina based on Near Infrared Spectroscopy (NIR) analysis and alveographic indices

Authors Patrizia Firmania, Alessandro Nardecchiaa, Francesca Nocenteb, Laura Gazzab, Federico Marinia, Alessandra Biancolilloa,c* aDepartment

bCREA

of Chemistry, University of Rome “La Sapienza”, Piazzale Aldo Moro, 5, 00185 Rome, Italy

Research Centre for Engineering and Agro-Food Processing, Via Manziana, 30 00189 Rome, Italy

cDepartment

of Physical and Chemical Sciences, University of L'Aquila, Via Vetoio, Coppito, 67100, Italy

*Corresponding Author: dr. Alessandra Biancolillo Dept. of Chemistry University of Rome La Sapienza P.le Aldo Moro 5 I-00185 Rome Italy Tel +39 06 49913680 Fax +39 06 4969 3292 e-mail: [email protected]

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Abstract Durum wheat (Triticum turgidum ssp durum) is widely grown in the Mediterranean area. The semolina obtained by this grain is used to prepare pasta, couscous, and baked products all over the world. The growing area affects the characteristics of Durum wheat; consequently, it is relevant to trace this product. The present study aims at developing an analytical methodology which would allow tracing durum semolina harvested in 7 different Italian macro-areas. In order to achieve this goal, 597 samples of semolina have been analysed by Near Infrared Spectroscopy, and by measuring alveographic parameters. Eventually, the information collected have been handled by a multi-block classifier (SO-PLS-LDA) in order to predict the origin of samples. The proposed approach provided extremely satisfactory results (in external validation, on a test set of 140 objects), correctly classifying all samples according to their growing area, confirming it represents a suitable solution for tracing durum wheat semolina. Keywords Multi-block; SO-PLS; Classification; Triticum durum semolina; Durum wheat; alveographic indices, Near Infrared Spectroscopy (NIR); LDA

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1. Introduction Durum wheat (Triticum turgidum ssp durum) is a tetraploid species mainly grown in the Mediterranean area and in Middle East, which flour and semolina are used in bakery stuffs or for pasta, couscous and bulgur production. In Italy, due to the long tradition of durum wheat pasta making, it is important to identify the origin in order to valorise the quality of semolina and, as reported in the Official Gazette of the Italian Republic, based on the ordinance of July 26th, 2017, it is mandatory to indicate on the label of pasta box the growing Country of durum wheat. This led to the necessity of developing analytical methods to prevent possible food frauds which could affect both producers and consumers under different aspects (e.g., economic losses and food safety). As already demonstrated in many studies, it is possible to distinguish different wheat species and cultivars employing various analytical techniques. Electrophoresis (Bietz & Simpson, 1992) is one of the first and most used methodology to analyse wheat proteins, applicable for the cultivar identification. Chromatography is also used for the investigation of wheats: Gas Chromatography (GC) was employed for the profiling and analysis of volatile composition in order to demonstrate the correlation between a certain cultivar and the flavour of cooked pasta (Beleggia, Platani, Spano, Monteleone, & Cattivelli 2009); liquid chromatography was used to estimate the colour of wheat present in different levels due to botanical origins and growing conditions (Fratianni, Irano, Panfili & Acquistucci, 2005), to detect adulterations (McCarthy, Scanlon, Lumley & Griffin, 1990), and to investigate the gliadins in different wheat cultivars (Burnouf & Bietz, 1984). Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) and X-ray fluorescence were used to extrapolate the fingerprints of geographical origin by different concentration of 22 elements due to the provenance soils (Zhao, Guo, Wei & Zhang, 2013a) also in combination with Scanning Electron Microscopy (SEM) analysis (Beltrami et al, 2011). Furthermore, Nuclear Magnetic Resonance (NMR) and Isotope Ratio Mass Spectrometry (IRMS) were employed to study geographical origin of wheat on the basis of its metabolic profile (Longobardi, Sacco, Casiello, Ventrella & Sacco, 2015). Despite the development of technology and the increasing capacities of the instruments, it is not straightforward to handle the information provided by such a complex matrix, as wheat is. Consequently, in the literature, several methodologies, developed by the combination of analytical techniques and chemometric approaches, have been proposed. For instance, Principal Component Analysis (PCA) was used together with Gas ChromatographyMass Spectrometry (GC-MS) and High-Performance Liquid Chromatography (HPLC) data in order to investigate the furosine and flavour compounds for the characterization of durum wheat pasta (Giannetti,

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Boccacci Mariani, Mannino & Testani, 2014). Alveographic indices have been used to assess the dough strength and to predict the quality of wheat flour; for instance, Marti et al. (2014) have inspected them, together with other rheological approaches, by correlation analysis, in order to evaluate characteristics of durum wheat semolina (Marti, Cecchini, D’Egidio, Dreisoerner, & Pagani, 2014). NMR and IRMS were also coupled with chemometric methods (PCA and discriminant analysis) to assess the geographical origin and quality of durum wheat, combining isotopic 13C/12C, 18O/16O, 15N/14N ratios and polysaccharides and triacylglycerols NMR data (Consonni & Cagliani, 2010). ANalysis Of VAriance (ANOVA) algorithm was used in an isotopic composition study in order to verify the importance of the geographical origin to distinguish different wheat samples (Brescia et al, 2002). Despite the efficiency of these approaches, they present a common drawback: the preparation of samples can be time-consuming, as well as economically and environmentally disadvantageous. Chemometrics allows the interpretation and a more complete exploitation of Near-Infrared (NIR) spectroscopy data: a faster, relatively cheap, non-destructive, non-invasive and automatable methodology. This spectroscopic technique is widely applied for the quality analysis of agro-food, in particular in combination with classification approaches to achieve diverse goals (for instance, to determine salubrity (Biancolillo, Firmani, Bucci, Magrì, & Marini 2019a), for on-line quality assessment (Pérez-Marín, Torres, Entrenas, Vega, & Sánchez, 2019), or to authenticate PDO foodstuff (Biancolillo et al, 2018a) and high-added value products (Eisenstecken et al. 2019; Firmani, De Luca, Bucci, Marini & Biancolillo, 2019a; Firmani, Bucci, Marini & Biancolillo, 2019b). Concerning the application of NIR for grain process control, several studies, aiming at different goals, are present in the literature. Partial Least Squares regression (PLS) has been applied on NIR data collected on wheat to predict milling and baking parameters such as flour extraction, protein content, gas and baking volume of different varieties (Jirsa, Hrušková, & Švec, 2008). Furthermore, this technique has been used, in combination with Discriminant Partial Least Squares (DPLS) analysis, to trace wheat grains, achieving acceptable results (~80% of correct classification rates) (Gonzáles-Martín et al, 2014); similarly, LDA and DPLS were also applied for assessing the origin of different wheat samples (Zhao, Guo, Wei, & Zhang, 2013b). In another study, PCA was employed to follow in-line agglomeration process of durum wheat semolina using different water supply conditions (Mandato, Taliani, Ait-Kaddour, Ruiz, & Cuq, 2013). Despite the great impact that chemometrics had on the obtainable results and the interpretation of data, is still difficult to obtain evermore reliable results, exploitable from a commodity point of view. Under this perspective, in the last years, for several reasons (for instance, relatively higher availability of analytical instruments/facilities), it has become not uncommon to handle multi-platform data sets. In principle, handling

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these kinds of data sets, the diverse data blocks could be individually examined, but it has been demonstrated that it could be more powerful to handle them by means of data fusion strategies. In the literature, several multi-block methods have been proposed; the main difference among them is the level data are fused. In fact, these approaches are often divided into low-level methodologies, where original data are joint, mid-level approaches, where the fusion takes place at the features level, and high-level strategies, which exploit a posteriori probability (Biancolillo, Boqué, Cocchi & Marini, 2019b). These approaches have been quite widely used for food-quality assessment; for instance, on tomatoes (Hohmann et al, 2015), fruits juices (Haddi et al, 2014), beer (Biancolillo, Bucci, Magrì, Magrì & Marini, 2014), wine (Silvestri et al, 2014; Biancolillo, Næs, Bro, & Måge, 2017) and several other foodstuffs. Accordingly, the aim of this work is to classify different cultivars of durum wheat grown in seven geographical areas of Italy by means of NIR spectra, alveographic parameters and a data-fusion classification approach. To achieve this goal, Sequential and Orthogonalized-Partial Least Squares-Linear Discriminant Analysis (SO-PLSLDA) has been exploited. This multi-block approach has been applied because it has demonstrated to perform well in terms of predictions and, at the same time, it is very suitable for the interpretation of the systems.

2. Materials and methods 2.1 Samples Semolina samples from 30 Triticum turgidum ssp durum cultivars were grown at 7 locations in Italy in 2016-17 using a randomized block design with 3 replications. Cultivars are divided into 8 geographical groups, according to their macro-areas of provenance (Po Valley, Adriatic coast, Rome, Central Apennines, South Apennines, Adriatic-Ionic, Sicily and Sardinia). Samples belonging to group 3 were not available. Details about all the analyzed samples are reported in Table 1. Wheat cultivars are listed in Table 2. -----------------------------------------Insert Table 1 approx. here--------------------------------------------------------------------------------Insert Table 2 approx. here----------------------------------------

2.2 Spectra acquisition

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NIR spectra were collected by means of a Nicolet 6700 FT-NIR instrument (Thermo Scientific Inc., Madison, WI) equipped with an InGaAs detector and an integrating sphere (Thermo Scientific Inc., Madison, WI); signals have been collected in reflectance mode in the range from 4000 cm−1–10000 cm−1 with a nominal resolution of 4 cm−1. Spectra were acquired directly on semolina samples (avoiding any physical-chemical pretreatment), which were put in glass vials and then located on the window of the integrating sphere. For each of the 3 replicated plot samples, spectra were collected in duplicate. Finally, signals were acquired by the OMNIC software (Thermo Scientific Inc., Madison, WI) and exported in MATLAB 2015b (The Mathworks, Natick, MA) for the subsequent analysis. Prior the creation of any classification model, spectra were converted into pseudo-absorbance (log(1/R)) and replicates were averaged leading to a data matrix of dimensions 597×3112. 2.3 W-index measurement Wheat kernels were milled using a Bühler MLU 202 laboratory mill (Bühler; Uzwil, Switzerland) to obtain semolina samples with a particle size lower than 500 µm, as established by AACC International, 2000 method 26e41. Alveographic parameters (P, L and W) of milled samples were analyzed by the Chopin Alveograph (Chopin, Villeneuve La Garenne, France) according to the UNI 10453 (1995). In the present work, only W-index (x 10-4 J) has been considered and the values (listed following the same order used in Table 2) are reported in the supplementary material.

2.4 Sequential and Orthogonalized-Partial Least Squares-Linear Discriminant Analysis Sequential and Orthogonalized-Partial Least Squares-Linear Discriminant Analysis (SO-PLS-LDA) (Biancolillo, Måge & Næs, 2015) is a data fusion classification method developed combining Linear Discriminant Analysis (LDA) (Fisher, 1936) with the multi-block regression method called Sequential and Orthogonalized-Partial Least Squares (SO-PLS) (Biancolillo & Næs, 2019c; Næs, Tomic, Mevik & Martens, 2011). This approach exploits SO-PLS as a feature reduction step, in order to allow the calculation of LDA even when the variance/covariance matrix is ill-conditioned (Biancolillo et al, 2015). Considering two predictor blocks 𝑿 and 𝒁, used to predict a 𝒀 response, the algorithm can be summarized into the following 5 steps: 1) 𝑿 is used to estimate the 𝒀 by PLS.

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In this step, model parameters (i.e., the PLS regression coefficients), as well as the 𝑿-scores (𝑻𝑋) and the 𝒀residuals (𝑬𝑌), are calculated. 2) 𝒁 is orthogonalized with respect to the 𝑿-scores (𝑻𝑋), obtaining 𝒁𝑂𝑟𝑡ℎ. This step is “the core” of the method; it removes the redundancies between the predictors. 3) 𝒁𝑂𝑟𝑡ℎ is used to estimate the 𝑬𝑌 by PLS. Model parameters (PLS regression coefficients), as well as the 𝒁𝑂𝑟𝑡ℎ-scores (𝑻𝒁𝑂𝑟𝑡ℎ), are calculated. 4) Calculation of the full predictive model. The general regression equation 𝒀 = 𝑿𝒃 + 𝒁𝒄 can be solved summing predictions from step 1 and step 3. 5) LDA can be either applied con the row-augmented scores (𝑻𝑆𝑂=[𝑻𝑋 𝑻𝒁𝑂𝑟𝑡ℎ]) or on the 𝒀 estimated in step 4. In a classification context, the 𝒀 response is the so-called Dummy matrix, a binary structure encoding the classinformation; examples about the generation of this matrix can be found in (Biancolillo & Marini, 2018b); a Matlab

function

for

the

calculation

of

SO-PLS

models

can

be

freely

downloaded

at

https://www.chem.uniroma1.it/romechemometrics/research/algorithms/so-pls/.

3. Results and discussion Raw NIR spectra of all the samples are presented in Figure 1. ------------------------------Insert Figure 1 approx. here--------------------------------------------------

Prior to the classification, data were imported in MatLab 2015b (The Mathworks, Natick, MA). As abovementioned, the aim of the present work is to develop an analytical methodology, based on the analysis of NIR and W indices, finalized at discriminating durum wheat semolina according to its geographical origin. In order to externally validate the classification model, it is necessary to divide the available samples into a training and a test set, which is not a trivial task in a multi-block framework. In fact, dividing objects into the two sets is necessary to take into account the sample variability present in all data blocks. Consequently, in order to create calibration and validation sets which could be a good representation of all the categories (i.e., which would cover

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as much as possible the sample space) accounting both data blocks under study, the following procedure has been pursued. NIR signals and W-indices have been divided in diverse data matrices according to the class-belonging of samples they are collected on (obtaining seven matrices for NIR spectra: 𝑿𝑁𝐼𝑅,1, 𝑿𝑁𝐼𝑅,2, 𝑿𝑁𝐼𝑅,3,𝑿𝑁𝐼𝑅,4,𝑿𝑁𝐼𝑅,5, 𝑿𝑁𝐼𝑅,6 and 𝑿𝑁𝐼𝑅,7 and seven for W-indices: 𝑾1, 𝑾2, 𝑾3,𝑾4,𝑾5,𝑾6 and 𝑾7 - one per each category ). Then, matrices are row-augmented class per class (for instance, for Class Po, 𝑪1 = [𝑿𝑁𝐼𝑅,1 𝑾1]) and Principal Component Analysis (PCA) (Joliffe, 2011) is calculated on each concatenated data matrix. Afterwards, the first 5 Principal Components (PCs) from each model are extracted and samples are divided by the Duplex algorithm (Snee, 1977). A graphical representation of this procedure applied to a generic Class 1 can be found in Figure 2. ------------------------------Insert Figure 2 approx. here------------------------------------------------------

Eventually, all the single-class training and test sub-sets are collected together, ending up with a calibration set consisting of 457 samples (70 belonging to Class Po, 70 to Adriatic, 61 appertaining to Class Central, 61 to Class South, 70 from Class Adriatic-Ionic, 67 belonging to Class Sicily and 58 to Class Sardinia), while the validation one is made of 20 samples for each category (for a total of 140 objects). At first, before adopting a multi-block approach by building the SO-PLS model, the possibility of using the individual blocks to trace the available samples was also tested. In particular, given the different nature of the blocks, PLS-LDA was used to build a classification model based on NIR spectra, while LDA was directly applied on the vector of W-indices. The PLS-LDA model built on the NIR data provided good classification rates in external validation; in fact, it led to an overall correct prediction rate on the test set slightly lower than 98% (3 samples misclassified). On the other hand, when the model was built on the W-index only, predictions did not result satisfactory. In fact, LDA misclassified about 30% of the test samples. The SO-PLS model has been calculated in a 7-fold cross-validation procedure. NIR spectra were used as first input block and W indices as the second one; both of them were mean centered (MC) in order to correct possible spurious variance due to the off-set differences accountable as the average trend. The SO-PLS calibration model provided 100% of correct classification (in cross-validation) for all the seven categories under study. Eventually, the calibration model was applied on the mean centered validation set, and, also in this case 100% of samples were correctly classified. In Figure 3, samples are projected onto the canonical variate space.

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As expected, the seven groups appear well divided, except for a slight overlapping between Class Adriatic-Coast (blue squares) and Class Adriatic-Ionic (black stars) which fall at similar values for the first two canonical variates, but they are distinguishable along the third one; this observation is not surprising considering these categories include grains very close to one and another from the geographical point of view. ------------------------------Insert Figure 3 approx. here--------------------------------------------------

Additionally, in order to have a deeper insight into the data set under study, Variable Importance in Projection (VIP) (Wold, Johansson, & Cocchi, 1993) indices were calculated (following the embedded procedure suggested in (Biancolillo Liland, Måge, Næs & Bro, 2016)), in order to understand which spectral bands gave the highest contribution in discriminating the groups. VIP coefficients weight the importance of each spectral variable in defining the LVs subspace; the average of the square values of the VIP indices is 1, thus meaning that this value can be assumed as a cut-off to define which spectral variables are the most significant. Starting from this assumption, all the variables presenting a VIP value higher than 1 are considered the most relevant for the classbelonging prediction. A graphical representation of VIP coefficients is reported in Figure 4. The red points correspond to spectral variables having VIP scores higher than one, while the black line is the average trainingset spectrum. ------------------------------Insert Figure 4 approx. here--------------------------------------------------

As reported in Figure 4, the most relevant spectral zones are: 

From 4000 to ~5500 cm-1 where combination bands of N-H bonds, O-H bonds, C-H bonds and C=O bonds are present (Cocchi et al, 2005)



From ~6700 to ~7400 cm-1 where the first overtone of the O–H stretching is present (Zhao, Guo, Wei, & Zhang, 2013b)



From ~9800 to ~10000 cm-1, where N–H 2nd overtone associated with peptides and proteins and 2nd overtone of C–H stretch (methyl and methylene) of starches, lipids and/or proteins are present (Zhao, Guo, Wei, & Zhang, 2013b).

4. Conclusions

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In conclusion, NIR spectroscopy and W-indices combined with SO-PLS-LDA in a multi-block strategy has demonstrated to be a suitable methodology for the assessment of the geographical origin of Italian Triticum durum semolina. In fact, the proposed approach allowed tracing all the available samples providing 100% of correct classification rates (in external validation).

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Aknowledgements The Authors thank C. Cecchini, E. Gosparini and F. Quaranta of CREA IT for technical assistance.

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Zhao, H., Guo, B., Wei, Y., & Zhang, B. (2013a). Multi-element composition of wheat grain and provenance soil and their potentialities as fingerprints of geographical origin. Journal of Cereal Science, 57, 391-397.

Zhao, H., Guo, B., Wei, Y., & Zhang, B. (2013b). Near infrared reflectance spectroscopy for the determination of the geographical origin of wheat. Food Chemistry, 138, 1902-1907.

16

Figure Captions Fig. 1: Raw NIR spectra of semolina samples Fig. 2: Graphical representation of the sets splitting procedure for a generic Class 1 Fig. 3: SO-PLS-LDA analysis: Samples projected onto the space of the first 2 canonical variates. Legend: Red circles: Class Po; Blue squares: Class Adriatic-Coast; Magenta triangles: Class Central; Cyan triangles: Class South; Black stars: Adriatic-Ionic; Green hexagrams: Class Sicily; Yellow triangles: Sardinia Fig. 4: VIP analysis of NIR spectra. Legend: black line - average spectrum; red points –variables presenting VIP>1

17

Table 1: Description of the analysed semolina samples Geographical origin

Class Name

Group

Number of lots

Number

number

available

samples

of

Po Valley

Po

1

30

90

Adriatic coast

Adriatic

2

30

90

Rome

--

3

NOT

NOT

AVAILABLE

AVAILABLE

27

81

Central Apennines

Central

4

18

South Apennines

South

5

27

81

Adriatic-Ionic

Adriatic-Ionic

6

30

90

Sicily

Sicily

7

29

87

Sardinia

Sardinia

8

26

78 597

19

Table 2: Investigated cultivars Cultivars

Cultivars Group 2

Group 1

Cultivars

Cultivars

Cultivars

Cultivars

Cultivars

Group 4

Group 5

Group 6

Group 7

Group 8

1.

Acadur

1.

Acadur

1.

Acadur

(2 lots)

2.

Alemanno

2.

Alemanno

1.

Achille

1.

Achille

1.

Achille

1.

Acadur

2.

Antalis

2.

Antalis

2.

Augusto

2.

Alemanno

3.

Augusto

3.

Augusto

3.

Calò

3.

Antalis

2.

Alemanno

3.

Antalis

3.

Antalis

4.

Calò

4.

Calò

4.

Claudio

4.

Aureo

3.

Antalis

4.

Aureo

4.

Aureo

5.

Claudio

5.

Claudio

5.

Core

5.

Burgos

4.

Aureo

5.

Burgos

5.

Calò

6.

Core

6.

Core

6.

Daurur

6.

Calò

5.

Burgos

6.

Calò

6.

Claudio

7.

Daurur

7.

Daurur

7.

Duilio

7.

Claudio

6.

Calò

7.

Claudio

7.

Core

8.

Diamante

8.

Diamante

8.

Ettore

8.

Core

7.

Claudio

8.

Core

8.

Duilio

9.

Duilio

9.

Duilio

9.

Furio

9.

Duilio

8.

Core

9.

Duilio

9.

Egeo

Camillo

10. Egeo

9.

Duilio

10. Egeo

10. Ettore 11. Furio

10. Dylan

10. Dylan

11. Ettore

11. Ettore

10. Iride

11. Ettore

10. Egeo

11. Ettore

12. Furio

12. Furio Camillo

11. Kanakis

12. Furio

11. Ettore

12. Furio

13. Iride

12. Marakas

13. Iride

14. Kanakis

13. Marco

14. Kanakis

15. Marakas

15. Marakas

16. Marco Aurelio

16. Marco

Camillo

Camillo 13. Iride

12. Furio Camillo

Camillo

Camillo 12. Iride

13. Iride

13. Kanakis

14. Kanakis

13. Iride

14. Kanakis

14. Karalis

14. Mario

15. Marakas

14. Kanakis

15. Marakas

15. Marakas

17. Mario

15. Monastir

16. Marco

15. Marakas

16. Marco

16. Marco

18. Monastir

16. Obelix

17. Mario

19. Obelix

17. Odisseo

17. Monastir

18. Monastir

20. Odisseo

18. Pigreco

18. Odisseo

19. Obelix

21. Pigreco

19. Ramirez

20. Odisseo

22. Ramirez

21. Pigreco

23. Santo Graal

22. Ramirez

24. Saragolla

23. Santo

25. Secolo

Aurelio

Aurelio

Aurelio

16. Marco

Aurelio

Aurelio

17. Monastir

17. Monastir

17. Monastir

18. Odisseo

18. Odisseo

19. Pigreco

18. Odisseo

19. Pigreco

19. Pigreco

20. Santo

20. Prospero

19. Pigreco

20. Prospero

20. Ramirez

Graal

21. Ramirez

20. Prospero

21. Ramirez

21. Salgado

21. Saragolla

22. Secolo

21. Ramirez

22. Salgado

22. Secolo

22. Secolo

23. Simeto

22. Salgado

23. Saragolla

23. Simeto

20

Aurelio

Graal

26. Simeto

23. Simeto

24. Svevo

23. Saragolla

24. Secolo

24. Svevo

24. Saragolla

27. Solstizio

24. Solstizio

25. Teodorico

24. Secolo

25. Simeto

25. Tirex

25. Secolo

28. Svevo

25. Svevo

26. Tirex

25. Simeto

26. Svevo

26. Tito

26. Simeto

29. Tirex

26. Tirex

27. Tito

26. Svevo

27. Teodorico

27. Solstizio

30. Tito Flavio

27. Tito

27. Teodorico

28. Tirex

28. Tirex

29. Tito

28. Svevo

Flavio

Flavio

29. Tirex

29. Tito

30. Tito

Flavio

Flavio

21

Flavio

Flavio

Highlights 

The quality of Durum wheat semolina is affected by the growing area



597 samples of semolina have been analysed by Near Infrared Spectroscopy (NIR)



Alveographic parameters have been measured on the same samples.



A data fusion approach (SO-PLS-LDA) was used to predict the origin of samples



All test samples (140 objects) were correctly classified

22