Optimization of Informative Spectral Regions in FT-NIR Spectroscopy for Measuring the Soluble Solids Content of Apple

References

• BrownP. J. (1992). Wavelength selection in multicomponent near-infrared calibration. Journal of Chemometrics, 6, 151–161. doi:10.1002/cem.1180060306.
   Web of Science ®Google Scholar
• BureauS., RuizD., ReichM., GoubleB., BertrandD., AudergonJ., & RenardC. M. (2009). Rapid and non-destructive analysis of apricot fruit quality using FT-near-infrared spectroscopy. Food Chemistry, 113, 1323–1328. doi:10.1016/j.foodchem.2008.08.066.
   Web of Science ®Google Scholar
• CarlomagnoG., CapozzoL., AttolicoG., & DistanteA. (2004). Non-destructive grading of peaches by near-infrared spectrometry. Infrared Physics & Technology, 46, 23–29. doi:10.1016/j.infrared.2004.03.004.
   Web of Science ®Google Scholar
• DuY., LiangY., JiangJ., BerryR., & OzakiY. (2004). Spectral regions selection to improve prediction ability of PLS models by changeable size moving window partial least squares and searching combination moving window partial least squares. Analytica Chimica Acta, 501, 183–191. doi:10.1016/j.aca.2003.09.041.
   Web of Science ®Google Scholar
• DurandA., DevosO., RuckebuschC., & HuvenneJ. (2007). Genetic algorithm optimisation combined with partial least squares regression and mutual information variable selection procedures in near-infrared quantitative analysis of cotton–viscose textiles. Analytica Chimica Acta, 595, 72–79. doi:10.1016/j.aca.2007.03.024.
   PubMed Web of Science ®Google Scholar
• FearnT. (2002). Discriminant analysis. In J. M.Chalmers & P. R.Griffiths (Eds.), Handbook of vibrational spectroscopy (pp. 2086–2093). Chichester: Wiley.
   Google Scholar
• PiernaJ. A., AbbasO., BaetenV., & DardenneP. (2009). A backward variable selection method for PLS regression (BVSPLS). Analytica Chimica Acta, 642, 89–93. doi:10.1016/j.aca.2008.12.002.
   PubMed Web of Science ®Google Scholar
• GoicoecheaH., & OlivieriA. (2002). Wavelength selection for multivariate calibration using a genetic algorithm: A novel initialization strategy. Analytical Chemistry, 42, 1146–1153.
   Google Scholar
• GoldingJ., SatyanS., LiebenbergC., WalshK., & McGlassonW. (2006). Application of portable NIR for measuring soluble solids content rations in peaches. Acta Horticulturae, 713, 461–464.
   Google Scholar
• GómezA., HeY., & PereiraA. (2006). Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using Vis/NIR-spectroscopy techniques. Journal of Food Engineering, 77, 313–319.
   Web of Science ®Google Scholar
• HanD., & WangJ. (2008). Review of nondestructive measurement of fruit quality by means of near infrared spectroscopy. Chinese Journal of Lasers, 35, 1123–1131. doi:10.3788/CJL20083508.1123.
   Google Scholar
• HuangL., HeD., & YangS. X. (2013). Segmentation on ripe fuji apple with fuzzy 2D entropy based on 2D histogram and GA optimization. Intelligent Automation & Soft Computing, 19, 239–251. doi:10.1080/10798587.2013.823755.
   Web of Science ®Google Scholar
• LammertynJ., NicolaïB., OomsK., De SmedtV., & De BaerdemaekerJ. (1998). Non-destructive measurement of acidity, soluble solids, and firmness of Jonagold apples using NIR-spectroscopy. Transactions of the ASAE, 41, 1089–1094. doi:10.13031/2013.17238.
   Google Scholar
• JhaS. N., JaiswalP., NarsaiahK., GuptaM., BhardwajR., & SinghA. K. (2012). Non-destructive prediction of sweetness of intact mango using near infrared spectroscopy. Scientia Horticulturae, 138, 171–175. doi:10.1016/j.scienta.2012.02.031.
   Web of Science ®Google Scholar
• JiangJ., BerryR. J., SieslerH. W., & OzakiY. (2002). Wavelength interval selection in multicomponent spectral analysis by moving window partial least-squares regression with applications to mid-infrared and near-infrared spectroscopic data. Analytical Chemistry, 74, 3555–3565. doi:10.1021/ac011177u.
   PubMed Web of Science ®Google Scholar
• KleynenO., LeemansV., & DestainM. (2003). Selection of the most efficient wavelength bands for ‘Jonagold’ apple sorting. Postharvest Biology and Technology, 30, 221–232. doi:10.1016/S0925-5214(03)00112-1.
   Web of Science ®Google Scholar
• LeardiR., & GonzálezA. (1998). Genetic algorithms applied to feature selection in PLS regression: How and when to use them. Chemometrics and Intelligent Laboratory Systems, 41, 195–207. doi:10.1016/S0169-7439(98)00051-3.
   Web of Science ®Google Scholar
• LiJ., HuangW., ZhaoC., & ZhangB. (2013). A comparative study for the quantitative determination of soluble solids content, pH and firmness of pears by Vis/NIR spectroscopy. Journal of Food Engineering, 116, 324–332. doi:10.1016/j.jfoodeng.2012.11.007.
   Web of Science ®Google Scholar
• LiaoQ., HuangY., HeS., XieR., LvQ., YiS., & … QianC. (2013). Cluster analysis of citrus genotypes using near-infrared spectroscopy. Intelligent Automation & Soft Computing, 19, 347–359. doi:10.1080/10798587.2013.824719.
   Web of Science ®Google Scholar
• LiuF., JiangY., & HeY. (2009). Variable selection in visible/near infrared spectra for linear and nonlinear calibrations: A case study to determine soluble solids content of beer. Analytica Chimica Acta, 635, 45–52. doi:10.1016/j.aca.2009.01.017.
   PubMed Web of Science ®Google Scholar
• LiuY., YingY., YuH., & FuX. (2006). Comparison of the HPLC method and FT-NIR analysis for quantification of glucose, fructose, and sucrose in intact apple fruits. Journal of Agricultural and Food Chemistry, 54, 2810–2815. doi:10.1021/jf052889e.
   PubMed Web of Science ®Google Scholar
• LucasiusC., & KatemanG. (1991). Genetic algorithms for large-scale optimization in chemometrics: An application. TrAC Trends in Analytical Chemistry, 10, 254–261. doi:10.1016/0165-9936(91)85132-B.
   Web of Science ®Google Scholar
• MagwazaL. S., OparaU. L., NieuwoudtH., CronjeP. J. R., SaeysW., & NicolaïB. (2012). NIR spectroscopy applications for internal and external quality analysis of citrus fruit—a review. Food and Bioprocess Technology, 5, 425–444. doi:10.1007/s11947-011-0697-1.
   Web of Science ®Google Scholar
• MendozaF., LuR., & CenH. (2012). Comparison and fusion of four nondestructive sensors for predicting apple fruit firmness and soluble solids content. Postharvest Biology and Technology, 73, 89–98. doi:10.1016/j.postharvbio.2012.05.012.
   Web of Science ®Google Scholar
• NicolaïB., BeullensK., BobelynE., PeirsA., SaeysW., TheronK., & LammertynJ. (2007). Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biology and Technology, 46, 99–118.
   Web of Science ®Google Scholar
• NicolaïB., VerlindenB., DesmetM., SaevelsS., SaeysW., TheronK., & … TorricelliA. (2008). Time-resolved and continuous wave NIR reflectance spectroscopy to predict soluble solids content and firmness of pear. Postharvest Biology and Technology, 47, 68–74.
   Google Scholar
• NørgaardL., SaudlandA., WagnerJ., NielsenJ., MunckL., & EngelsenS. (2000). Interval partial least squares regression (iPLS): A comparative chemometric study with an example from near-infrared spectroscopy. Applied Spectroscopy, 54, 413–419.
   Web of Science ®Google Scholar
• OsborneB., & FearnT. (1988). Near infrared spectroscopy in food analysis. New Jersey, USA: John Wiley & Sons.
   Google Scholar
• OzakiY., McClureW., & ChristyA. (2006). Near-infrared spectroscopy in food and technology. New Jersey: John Wiley & Sons.
   Google Scholar
• PazP., SánchezM., Pérez-MarínD., GuerreroJ. E., & Garrido-VaroA. (2009). Instantaneous quantitative and qualitative assessment of pear quality using near infrared spectroscopy. Computers and Electronics in Agriculture, 69, 24–32. doi:10.1016/j.compag.2009.06.008.
   Web of Science ®Google Scholar
• PeirsA., LammertynJ., OomsK., & NicolaıB. M. (2001). Prediction of the optimal picking date of different apple cultivars by means of VIS/NIR-spectroscopy. Postharvest Biology and Technology, 21, 189–199. doi:10.1016/S0925-5214(00)00145-9.
   Web of Science ®Google Scholar
• Pérez-MarínD., SánchezM., PazP., SorianoM., GuerreroJ., & Garrido-VaroA. (2009). Non-destructive determination of quality parameters in nectarines during on-tree ripening and postharvest storage. Postharvest Biology and Technology, 52, 180–188.
   Web of Science ®Google Scholar
• PiF., ShinzawaH., WangJ., HanD., & OzakiY. (2009). Partial least square regression with variable-bagged model ensemble. Journal of Near Infrared Spectroscopy, 17, 33–40. doi:10.1255/jnirs.824.
   Web of Science ®Google Scholar
• SaranwongS., SornsrivichaiJ., & KawanoS. (2001). Improvement of PLS calibration for Brix value and dry matter of mango using information from MLR calibration. Journal of Near Infrared Spectroscopy, 9, 287–295. doi:10.1255/jnirs.314.
   Web of Science ®Google Scholar
• SchaareP., & FraserD. (2000). Comparison of reflectance, interactance and transmission modes of visible-near infrared spectroscopy for measuring internal properties of kiwifruit (Actinidia chinensis). Postharvest Biology and Technology, 20, 175–184. doi:10.1016/S0925-5214(00)00130-7.
   Web of Science ®Google Scholar
• SpiegelmanC. H., McShaneM. J., GoetzM. J., MotamediM., YueQ. L., & CotéG. L. (1998). Theoretical justification of wavelength selection in PLS calibration: Development of a new algorithm. Analytical Chemistry, 70, 35–44. doi:10.1021/ac9705733.
   PubMed Web of Science ®Google Scholar
• SubediP., WalshK., & OwensG. (2007). Prediction of mango eating quality at harvest using short-wave near infrared spectrometry. Postharvest Biology and Technology, 43, 326–334. doi:10.1016/j.postharvbio.2006.09.012.
   Web of Science ®Google Scholar
• WangJ., & HanD. (2008). Analysis of near infrared spectra of apple SSC by genetic algorithm optimization. Spectroscopy and Spectral Analysis, 28, 2308–2311.
   Web of Science ®Google Scholar
• WilliamsP., & NorrisK. (1987). Near-infrared technology in the agricultural and food industries. St Paul, USA: American Association of Cereal Chemists.
   Google Scholar
• XiaoboZ, JiewenZ, XingyiH, & YanxiaoL (2007). Use of FT-NIR spectrometry in non-invasive measurements of soluble solid contents (SSC) of ‘Fuji’ apple based on different PLS models. Chemometrics and Intelligent Laboratory Systems, 87, 43–51. doi:10.1016/j.chemolab.2006.09.003.
   Web of Science ®Google Scholar
• XuL., & SchechterI. (1996). Optimization method for simultaneous kinetic analysis. Analytical Chemistry, 68, 1842–1850. doi:10.1021/ac951061w.
   PubMed Web of Science ®Google Scholar
• Yi-ZengL, Yu-LongX, & Ru-QinY (1989). Accuracy criteria and optimal wavelength selection for multicomponent spectrophotometric determinations. Analytica Chimica Acta, 222, 347–357. doi:10.1016/S0003-2670(00)81909-1.
   Web of Science ®Google Scholar
• YingY., & LiuY. (2008). Nondestructive measurement of internal quality in pear using genetic algorithms and FT-NIR spectroscopy. Journal of Food Engineering, 84, 206–213. doi:10.1016/j.jfoodeng.2007.05.012.
   Web of Science ®Google Scholar
• ZhangL., XuH., & GuM. (2014). Use of signal to noise ratio and area change rate of spectra to evaluate the Visible/NIR spectral system for fruit internal quality detection. Journal of Food Engineering, 139, 19–23. doi:10.1016/j.jfoodeng.2014.04.009.
   Web of Science ®Google Scholar
• ZhuD., JiB., MengC., ShiB., TuZ., & QingZ. (2007). The performance of ν-support vector regression on determination of soluble solids content of apple by acousto-optic tunable filter near-infrared spectroscopy. Analytica Chimica Acta, 598, 227–234. doi:10.1016/j.aca.2007.07.047.
   PubMed Web of Science ®Google Scholar
• ZudeM., HeroldB., RogerJ., Bellon-MaurelV., & LandahlS. (2006). Non-destructive tests on the prediction of apple fruit flesh firmness and soluble solids content on tree and in shelf life. Journal of Food Engineering, 77, 254–260. doi:10.1016/j.jfoodeng.2005.06.027.
   Web of Science ®Google Scholar