Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
SpringerLink
Log in

Calibration Transfer from Micro NIR Spectrometer to Hyperspectral Imaging: a Case Study on Predicting Soluble Solids Content of Bananito Fruit (Musa acuminata)

  • Published:
Food Analytical Methods Aims and scope Submit manuscript

Abstract

Calibration transfer from a handheld micro NIR spectrometer (NIR-point, 939–1602 nm, 6.2 nm) to a desktop hyperspectral imaging (NIR-HSI) for predicting soluble solids content (SSC) of bananito flesh was investigated in the study. Different spectral pre-processing and standardization methods were employed for correcting spectra so as to minimise spectral differences between NIR-point and NIR-HSI. Results show that application of standard normal variate (SNV) reduced spectral differences from 31.49 to 8.96%. The best standardization method was developed based on piecewise direct standardization (PDS) algorithm using ten transfer samples. The developed PLS model yielded a high prediction performance (R 2 p = 0.922 and RMSEP = 1.451%) for predicting SSC of validation samples using the NIR-point spectra. After SNV and standardization, the model was successfully transferred to NIR-HSI data, giving a comparable prediction accuracy of R 2 p = 0.925 and RMSEP = 1.592%. The results illustrated the potential of transferring calibration models from a simple and easy-available micro NIR spectrometer to a more expensive and sophisticated hyperspectral imaging system, when the spatial distribution of quality information is required.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Ahmad S, Clarke B, Thompson A (2001) Banana harvest maturity and fruit position on the quality of ripe fruit. Ann Appl Biol 139:329–335

    Article  Google Scholar 

  • Amigo JM, Babamoradi H, Elcoroaristizabal S (2015) Hyperspectral image analysis. A tutorial. Anal Chim Acta 896:34–51

    Article  CAS  Google Scholar 

  • Antonucci F, Pallottino F, Paglia G, Palma A, D’Aquino S, Menesatti P (2011) Non-destructive estimation of mandarin maturity status through portable VIS-NIR spectrophotometer. Food Bioprocess Technol 4:809–813

    Article  Google Scholar 

  • Blankenship SM, Ellsworth DD, Powell RG (1993) A ripening index for banana fruit based on starch content. HortTechnology 3:338–339

    Google Scholar 

  • Bouveresse E, Hartmann C, Massart DL, Last IR, Prebble KA (1996) Standardization of near-infrared spectrometric instruments. Anal Chem 68:982–990

    Article  CAS  Google Scholar 

  • Calabrese, F. 1993. Frutticoltura tropicale e subtropicale I: fruttiferi erbacei e suffruticosi

  • Cheng J-H, Sun D-W (2015a) Rapid and non-invasive detection of fish microbial spoilage by visible and near infrared hypersZpectral imaging and multivariate analysis. Lwt-Food Science And Technology 62:1060–1068

    Article  CAS  Google Scholar 

  • Cheng J-H, Sun D-W (2017) Partial Least Squares Regression (PLSR) Applied to NIR and HSI Spectral Data Modeling to Predict Chemical Properties of Fish Muscle. Food Engineering Reviews 9:36–49

    Article  CAS  Google Scholar 

  • Cheng J-H, Sun D-W, Pu H, Zhu Z (2015b) Development of hyperspectral imaging coupled with chemometric analysis to monitor K value for evaluation of chemical spoilage in fish fillets. Food Chemistry 185:245–253

    Article  CAS  Google Scholar 

  • Cheng J-H, Sun D-W, Pu H (2016a) Combining the genetic algorithm and successive projection algorithm for the selection of feature wavelengths to evaluate exudative characteristics in frozen-thawed fish muscle. Food Chemistry 197:855–863

    Article  CAS  Google Scholar 

  • Cheng J-H, Sun D-W, Qu J-H, Pu H-B, Zhang X-C, Song Z, Chen X, Zhang H (2016b) Developing a multispectral imaging for simultaneous prediction of freshness indicators during chemical spoilage of grass carp fish fillet. Journal of Food Engineering 182:9–17

    Article  CAS  Google Scholar 

  • Cheng L, Sun D-W, Zhu Z, Zhang Z (2017) Emerging techniques for assisting and accelerating food freezing processes: A review of recent research progresses. Critical Reviews In Food Science and Nutrition 57:769–781

    Article  Google Scholar 

  • Cordenunsi BR, Lajolo FM (1995) Starch breakdown during banana ripening: sucrose synthase and sucrose phosphate synthase. J Agric Food Chem 43:347–351

    Article  CAS  Google Scholar 

  • Delgado AE, Sun D-W (2002) Desorption isotherms and glass transition temperature for chicken meat Journal Of Food Engineering Vol 55:1–8

    Google Scholar 

  • Du CJ, Sun D-W (2005) Pizza sauce spread classification using colour vision and support vector machines. Journal of Food Engineering Vol 66:137–145

    Article  Google Scholar 

  • Elmasry G, Sun D-W (2010) CHAPTER 1—principles of hyperspectral imaging technology. In: Sun D-W (ed) Hyperspectral imaging for food quality analysis and control. Academic Press, San Diego

    Google Scholar 

  • ElMasry G, Sun D-W, Allen P (2013) Chemical-free assessment and mapping of major constituents in beef using hyperspectral imaging. Journal of Food Engineering 117:235–246

    Article  CAS  Google Scholar 

  • Fearn T (2001) Standardisation and calibration transfer for near infrared instruments: a review. J Near Infrared Spectrosc 9:229–244

    Article  CAS  Google Scholar 

  • Feng Y-Z, Sun D-W (2013a) Near-infrared hyperspectral imaging in tandem with partial least squares regression and genetic algorithm for non-destructive determination and visualization of Pseudomonas loads in chicken fillets. Talanta 109:74–83

  • Feng Y-Z, ElMasry G, Sun D-W, Scannell Amalia GM, Des W, Morcy N (2013b) Near-infrared hyperspectral imaging and partial least squares regression for rapid and reagentless determination of Enterobacteriaceae on chicken fillets. Food Chemistry 138:1829–1836

  • Feudale RN, Woody NA, Tan H, Myles AJ, Brown SD, Ferré J (2002) Transfer of multivariate calibration models: a review. Chemom Intell Lab Syst 64:181–192

    Article  CAS  Google Scholar 

  • Firtha F, Fekete A, Kaszab T, Gillay B, Nogula-nagy M, Kovács Z, Kantor DB (2008) Methods for improving image quality and reducing data load of NIR hyperspectral images. Sensors (Basel, Switzerland) 8:3287–3298

    Article  CAS  Google Scholar 

  • Galvão RKH, Araujo MCU, José GE, Pontes MJC, Silva EC, Saldanha TCB (2005) A method for calibration and validation subset partitioning. Talanta 67:736–740

    Article  Google Scholar 

  • Ge Y, Morgan CLS, Grunwald S, Brown DJ, Sarkhot DV (2011) Comparison of soil reflectance spectra and calibration models obtained using multiple spectrometers. Geoderma 161:202–211

    Article  CAS  Google Scholar 

  • Gendrin C, Roggo Y, Collet C (2008) Pharmaceutical applications of vibrational chemical imaging and chemometrics: a review. J Pharm Biomed Anal 48:533–553

    Article  CAS  Google Scholar 

  • Gowen AA, O'Donnell CP, Cullen PJ, Downey G, Frias JM (2007) Hyperspectral imaging—an emerging process analytical tool for food quality and safety control. Trends Food Sci Technol 18:590–598

    Article  CAS  Google Scholar 

  • Grelet C, Fernández Pierna JA, Dardenne P, Baeten V, Dehareng F (2015) Standardization of milk mid-infrared spectra from a European dairy network. J Dairy Sci 98:2150–2160

    Article  CAS  Google Scholar 

  • Huang J, Romero-Torres S, Moshgbar M (2010) Practical considerations in data pre-treatment for NIR and Raman spectroscopy. Am Pharm Rev 13:116–127

    CAS  Google Scholar 

  • Jackman P, Sun D-W, Allen P (2009) Automatic segmentation of beef longissimus dorsi muscle and marbling by an adaptable algorithm. Meat Science 83:187–194

    Article  Google Scholar 

  • Jackman P, Sun D-W, Allen P (2011) Recent advances in the use of computer vision technology in the quality assessment of fresh meats. Trends In Food Science & Technology 22:185–197

    Article  CAS  Google Scholar 

  • Ji W, Viscarra Rossel RA, Shi Z (2015) Improved estimates of organic carbon using proximally sensed vis–NIR spectra corrected by piecewise direct standardization. Eur J Soil Sci 66:670–678

    Article  CAS  Google Scholar 

  • Kader AA (2002) Postharvest technology of horticultural crops, University of California, Agric Nat Resour

  • Kamruzzaman M, ElMasry G, Sun D-W, Allen P (2013) Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging. Food Chemistry 141:389–396

    Article  CAS  Google Scholar 

  • Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11:137–148

    Article  Google Scholar 

  • Kiani H, Zhang Z, Delgado A, Sun D-W (2011) Ultrasound assisted nucleation of some liquid and solid model foods during freezing. Food Research International 44:2915–2921

    Article  CAS  Google Scholar 

  • Li J-L, Sun D-W, Pu H, Jayas DS (2017) Determination of trace thiophanate-methyl and its metabolite carbendazim with teratogenic risk in red bell pepper (Capsicumannuum L.) by surface-enhanced Raman imaging technique. Food Chemistry 218:543–552

    Article  CAS  Google Scholar 

  • Liang C, Yuan H-F, Zhao Z, Song C-F, Wang J-J (2016) A new multivariate calibration model transfer method of near-infrared spectral analysis. Chemom Intell Lab Syst 153:51–57

    Article  CAS  Google Scholar 

  • Liu Z, Yu H, Macgregor JF (2007) Standardization of line-scan NIR imaging systems. J Chemom 21:88–95

    Article  Google Scholar 

  • Liu Y, Cai W, Shao X (2014) Standardization of near infrared spectra measured on multi-instrument. Anal Chim Acta 836:18–23

    Article  CAS  Google Scholar 

  • Liu D, Ma J, Sun D-W, Pu H, Gao W, Qu J, Zeng X-A (2014) Prediction of color and pH of salted porcine meats using visible and near-infrared hyperspectral imaging. Food Bioprocess Technol 7(11):3100–3108

    Article  CAS  Google Scholar 

  • Lorente D, Aleixos N, Gómez-Sanchis J, Cubero S, García-Navarrete OL, Blasco J (2012) Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food Bioprocess Technol 5(4):1121–1142

    Article  Google Scholar 

  • Luypaert J, Massart DL, Vander Heyden Y (2007) Near-infrared spectroscopy applications in pharmaceutical analysis. Talanta 72:865–883

    Article  CAS  Google Scholar 

  • Ma J, Pu H, Sun D-W, Gao W, Qu J-H, Ma K-Y (2015) Application of Vis-NIR hyperspectral imaging in classification between fresh and frozen-thawed pork Longissimus Dorsi muscles. International Journal of Refrigeration-Revue Internationale Du Froid 50:10–18

  • Ma J, Sun D-W, Pu H (2016) Spectral absorption index in hyperspectral image analysis for predicting moisture contents in pork longissimus dorsi muscles. Food Chemistry 197:848–854

    Article  CAS  Google Scholar 

  • Ma J, Sun D-W, Qu J-H, Pu H (2017) Prediction of textural changes in grass carp fillets as affected by vacuum freeze drying using hyperspectral imaging based on integrated group wavelengths. Lwt-Food Science and Technology 82:377–385

    Article  CAS  Google Scholar 

  • McDonald K, Sun D-W, Kenny T (2000) of the quality of cooked beef products cooled by vacuum cooling and by conventional cooling. Lebensmittel-Wissenschaft Und-Technologie-Food Science and Technology Vol 33:21–29

  • McDonald K, Sun D-W, Kenny T (2001) The effect of injection level on the quality of a rapid vacuum cooled cooked beef product. Journal of Food Engineering Vol 47:139–147

    Article  Google Scholar 

  • Mustaffa R, Osman A, Yusof S, Mohamed S (1998) Physico-chemical changes in Cavendish banana (Musa cavendishiiL var Montel) at different positions within a bunch during development and maturation. J Sci Food Agric 78:201–207

    Article  CAS  Google Scholar 

  • Nicolaï BM, Beullens K, Bobelyn E, Peirs A, Saeys W, Theron KI, Lammertyn J (2007) Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol Technol 46:99–118

    Article  Google Scholar 

  • Oliveri P, Casolino MC, Casale M, Medini L, Mare F, Lanteri S (2013) A spectral transfer procedure for application of a single class-model to spectra recorded by different near-infrared spectrometers for authentication of olives in brine. Anal Chim Acta 761:46–52

    Article  CAS  Google Scholar 

  • Osborne BG (2006) Near-infrared spectroscopy in food analysis. John Wiley & Sons, Ltd., Encyclopedia of Analytical Chemistry

    Google Scholar 

  • Pereira CF, Pimentel MF, Galvao RK, Honorato FA, Stragevitch L, Martins MN (2008) A comparative study of calibration transfer methods for determination of gasoline quality parameters in three different near infrared spectrometers. Anal Chim Acta 611:41–47

    Article  CAS  Google Scholar 

  • Pereira LSA, Carneiro MF, Botelho BG, Sena MM (2016) Calibration transfer from powder mixtures to intact tablets: a new use in pharmaceutical analysis for a known tool. Talanta 147:351–357

    Article  CAS  Google Scholar 

  • Perez-Guaita D, Ventura-Gayete J, Pérez-Rambla C, Sancho-Andreu M, Garrigues S, De La Guardia M (2012) Protein determination in serum and whole blood by attenuated total reflectance infrared spectroscopy. Anal Bioanal Chem 404:649–656

    Article  CAS  Google Scholar 

  • Pierna JAF, Vermeulen P, Lecler B, Baeten V, Dardenne P (2010) Calibration transfer from dispersive instruments to handheld spectrometers. Appl Spectrosc 64:644–648

    Article  CAS  Google Scholar 

  • Pojić MM, Mastilović JS (2013) Near infrared spectroscopy—advanced analytical tool in wheat breeding, trade, and processing. Food Bioprocess Technol 6:330–352

    Article  Google Scholar 

  • Pu Y-Y, Sun D-W (2016) Prediction of moisture content uniformity of microwave-vacuum dried mangoes as affected by different shapes using NIR hyperspectral imaging. Innovative Food Science & Emerging Technologies 33:348–356

  • Pu Y-Y, Sun D-W (2017) Combined hot-air and microwave-vacuum drying for improving drying uniformity of mango slices based on hyperspectral imaging visualisation of moisture content distribution. Biosystems Engineering 156:108–119

    Article  Google Scholar 

  • Pu H, Sun D-W, Ma J, Cheng J-H (2015a) Classification of fresh and frozen-thawed pork muscles using visible and near infrared hyperspectral imaging and textural analysis. Meat Science 99:81–88

    Article  Google Scholar 

  • Pu H, Kamruzzaman M, Sun D-W (2015b) Selection of feature wavelengths for developing multispectral imaging systems for quality, safety and authenticity of muscle foods-a review. Trends In Food Science & Technology 45:86–104

    Article  CAS  Google Scholar 

  • Qin Y, Gong H (2016) NIR models for predicting total sugar in tobacco for samples with different physical states. Infrared Phys Technol 77:239–243

    Article  CAS  Google Scholar 

  • Qu J-H, Sun D-W, Cheng J-H et al (2017) Mapping moisture contents in grass carp (Ctenopharyngodon idella) slices under different freeze drying periods by Vis-NIR hyperspectral imaging. lwt-food science and technology 75:529–536

  • Ram HM, Ram M, Steward F (1962) Growth and development of the banana plant: 3. A. The origin of the inflorescence and the development of the flowers: B. The structure and development of the fruit. Ann Bot 26:657–673

    Article  Google Scholar 

  • Ravikanth L, Jayas DS, White NDG, Fields PG, Sun D-W (2017) Extraction of spectral information from hyperspectral data and application of hyperspectral imaging for food and agricultural products. Food Bioprocess Technol 10(1):1–33

    Article  CAS  Google Scholar 

  • Rinnan Å, Berg FVD, Engelsen SB (2009) Review of the most common pre-processing techniques for near-infrared spectra. TrAC Trends Anal Chem 28:1201–1222

    Article  CAS  Google Scholar 

  • Robinson JC, Saúco VG (2010) Bananas and plantains. CABI

  • Schmutzler M, Huck CW (2016) Simultaneous detection of total antioxidant capacity and total soluble solids content by Fourier transform near-infrared (FT-NIR) spectroscopy: a quick and sensitive method for on-site analyses of apples. Food Control 66:27–37

    Article  CAS  Google Scholar 

  • Shenk JS, Westerhaus MO, Templeton WC (1985) Calibration transfer between near infrared reflectance spectrophotometers. Crop Sci 25:159–161

    Article  Google Scholar 

  • Sjöblom J, Svensson O, Josefson M, Kullberg H, Wold S (1998) An evaluation of orthogonal signal correction applied to calibration transfer of near infrared spectra. Chemom Intell Lab Syst 44:229–244

    Article  Google Scholar 

  • Soldado A, Fearn T, Martínez-Fernández A, De La Roza-Delgado B (2013) The transfer of NIR calibrations for undried grass silage from the laboratory to on-site instruments: comparison of two approaches. Talanta 105:8–14

    Article  CAS  Google Scholar 

  • Stanimirova I, Kubik A, Walczak B, Einax JW (2008) Discrimination of biofilm samples using pattern recognition techniques. Anal Bioanal Chem 390:1273–1282

    Article  CAS  Google Scholar 

  • Sun D-W (1997) Solar powered combined ejector vapour compression cycle for air conditioning and refrigeration. Energy Conversion and Management 38:479–491

    Article  CAS  Google Scholar 

  • Sun D-W (1999) Comparison and selection of EMC ERH isotherm equations for rice. Journal of Stored Products Research. Volume 35:249–264

    Article  Google Scholar 

  • Sun D-W, Brosnan T (1999) Extension of the vase life of cut daffodil flowers by rapid vacuum cooling. International Journal of Refrigeration-Revue Internationale Du Froid Vol 22:472–478

    Article  Google Scholar 

  • Sun D-W, Brosnan T (2003) Pizza quality evaluation using computer vision - Part 2 - Pizza topping analysis. Journal of Food Engineering Vol 57:91–95

    Article  Google Scholar 

  • Sun D-W, Zheng LY (2006) Vacuum cooling technology for the agri-food industry: Past, present and future. Journal of Food Engineering Vol 77:203–214

    Article  Google Scholar 

  • Tsuchikawa S (2007) A review of recent near infrared research for wood and paper. Appl Spectrosc Rev 42:43–71

    Article  CAS  Google Scholar 

  • Vidal M, Amigo JM (2012) Pre-processing of hyperspectral images. Essential steps before image analysis. Chemom Intell Lab Syst 117:138–148

    Article  CAS  Google Scholar 

  • Wang Y, Kowalski BR (1993) Temperature-compensating calibration transfer for near-infrared filter instruments. Anal Chem 65:1301–1303

    Article  CAS  Google Scholar 

  • Wang LJ, Sun D-W (2004) Effect of operating conditions of a vacuum cooler on cooling performance for large cooked meat joints. Journal of Food Engineering Vol 61:231–240

    Article  Google Scholar 

  • Wang Y, Veltkamp DJ, Kowalski BR (1991) Multivariate instrument standardization. Anal Chem 63:2750–2756

    Article  CAS  Google Scholar 

  • Wise BM, Gallagher NB, Bro R, Shaver JM, Windig W, Koch RS (2006) Chemometrics tutorial for PLS_Toolbox and Solo. Eigenvector Research, Inc., Wenatchee

    Google Scholar 

  • Woodcock T, Fagan CC, O’Donnell CP, Downey G (2008) Application of near and mid-infrared spectroscopy to determine cheese quality and authenticity. Food Bioprocess Technol 1:117–129

    Article  Google Scholar 

  • Woody NA, Feudale RN, Myles AJ, Brown SD (2004) Transfer of multivariate calibrations between four near-infrared spectrometers using orthogonal signal correction. Anal Chem 76:2595–2600

    Article  CAS  Google Scholar 

  • Wu D, Sun D-W (2013) Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review - Part II: Applications. Innovative Food Science & Emerging Technologies 19:15–28

    Article  Google Scholar 

  • Xie A, Sun D-W, Xu Z, Zhu Z (2015) Rapid detection of frozen pork quality without thawing by Vis-NIR hyperspectral imaging technique. Talanta 139:208–215

    Article  CAS  Google Scholar 

  • Xie A, Sun D-W, Zhu Z, Pu H (2016) Nondestructive Measurements of Freezing Parameters of Frozen Porcine Meat by NIR Hyperspectral Imaging. Food and Bioprocess Technology 9:1444–1454

    Article  CAS  Google Scholar 

  • Xiong Z, Sun D-W, Pu H, Xie A, Han Z, Luo M (2015) Non-destructive prediction of thiobarbituric acid reactive substances (TSARS) value for freshness evaluation of chicken meat using hyperspectral imaging. Food Chemistry 179:175–181

    Article  CAS  Google Scholar 

  • Xu J-L, Sun D-W (2017) Identification of freezer burn on frozen salmon surface using hyperspectral imaging and computer vision combined with machine learning algorithm. International Journal of Refrigeration-Revue Internationale Du Froid 74:151–164

    Article  Google Scholar 

  • Yang Q, Da-Wen S, Weiwei C (2017) Development of simplified models for nondestructive hyperspectral imaging monitoring of TVB-N contents in cured meat during drying process. Journal of food engineering 192:53–60

    Article  CAS  Google Scholar 

  • Zamora-Rojas E, Pérez-Marín D, De Pedro-Sanz E, Guerrero-Ginel JE, Garrido-Varo A (2012) Handheld NIRS analysis for routine meat quality control: database transfer from at-line instruments. Chemom Intell Lab Syst 114:30–35

    Article  CAS  Google Scholar 

  • Zheng LY, Sun D-W (2004) Vacuum cooling for the food industry - a review of recent research advances. Trends In Food Science & Technology Vol 15:555–568

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors would like to greatly acknowledge PhD students Le Wen, Kexin Zhang, and Sindhuraj Mukherjee, and the research group of Prof. Colm O’Donnell from School of Biosystems and Food Engineering, UCD for their help in image acquisition of bananitos using NIR-HSI system. The authors would also like to thank Annamaria Stellari for technical support and the AL.MA s.r.l. company for bananito fruit supply. UCD-CSC Scholarship Scheme supported by University College Dublin (UCD) and China Scholarship Council (CSC) was acknowledged for this study.

Author information

Authors and Affiliations

  1. School of Biosystems and Food Engineering, University College Dublin, National University of Ireland, Dublin 4, Ireland

    Yuan-Yuan Pu, Da-Wen Sun & Aoife Gowen

  2. Faculty of Agriculture and Forestry Engineering, Department of Animal Production, University of Cordoba, Campus Rabanales, Ctra.Nacional IV – Km 396, 14071, Cordoba, Spain

    Cecilia Riccioli

  3. Council for Agricultural Research and Economics (CREA-IT), via Venezian 26, 20133, Milan, Italy

    Marina Buccheri, Maurizio Grassi & Tiziana M. P. Cattaneo

Authors
  1. Yuan-Yuan Pu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Da-Wen Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cecilia Riccioli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marina Buccheri
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Maurizio Grassi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tiziana M. P. Cattaneo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Aoife Gowen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Da-Wen Sun.

Ethics declarations

Conflict of Interest

Yuan-Yuan Pu declares that she has no conflict of interest. Da-Wen Sun declares that he has no conflict of interest. Cecilia Riccioli declares that she has no conflict of interest. Marina Buccheri declares that she has no conflict of interest. Maurizio Grassi declares that he has no conflict of interest. Tiziana M.P. Cattaneo declares that she has no conflict of interest. Aoife Gowen declares that she has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants performed by any of the authors.

Informed Consent

Not applicable.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pu, YY., Sun, DW., Riccioli, C. et al. Calibration Transfer from Micro NIR Spectrometer to Hyperspectral Imaging: a Case Study on Predicting Soluble Solids Content of Bananito Fruit (Musa acuminata). Food Anal. Methods 11, 1021–1033 (2018). https://doi.org/10.1007/s12161-017-1055-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12161-017-1055-3

Keywords

Search

Navigation