**4. Conclusion**

224 Infrared Spectroscopy – Life and Biomedical Sciences

alternative for in-line and at-line environments. Another comparative study was conducted by Schmilovitch et al. (2001) for two relatively thin skin cultivars, 'Ettinger' and 'Fuerte', during a single season. They used a dispersive NIR spectrophotometer in reflectance mode in the 1200 - 2400 nm range, reporting errors of prediction for 'Ettinger' and 'Fuerte' of 0.9% and 1.3% respectively, for fruit having a 14 – 24 %DM range. It is likely that the relatively smooth to medium textured, thin-skin cultivars would not suffer to the same extent from the physiological limitations experienced in the thick rough skin of 'Hass', and prediction errors would certainly be expected to be lower. We must emphasize however, it is difficult to make a meaningful comparison of the various techniques as there is insufficient detail presented in these papers to establish if the differences are associated with the spectroscopic technique

Classification statistics for the prediction of percentage rot development are presented in Table 4. The preliminary study found that by applying discriminative analysis techniques, 92.8% of the test population could be correctly classified into 2 categories, above and below 30% rot development for the area scanned. The percentage correctly classified decreased slightly to 86.8% when the classification was reduced to above and below 10% rot

**LV Spectra** 

**misclassified (%)** 

8 7.2 (n=18) 92.8 (n=232)

9 13.6 (n=33) 86.8 (n=217)

**Spectra correctly classified (%)** 

**Defined classification (%)** 

(ii) 31-100

(ii) 11 – 100

whole 'Hass' avocado fruit. *Note: LV = latent variables; n = number of samples.* 

change over point of the two defined classification categories at 10% bruising.

Table 4. Classification statistics for prediction of percentage rot development (shelf life) of

Table 5 depicts the classification statistics for the prediction of percentage bruise development. The results indicate that 90% of the population could be correctly classified into 2 categories based on percentage bruise development in the scanned area (≤10%, ≥11%) using scans conducted 1 - 2 hours following impact. Of the 10 (9.8%) samples misclassified, 6 (5.9%) samples visually rated with bruising greater than 11% were placed into the <10% bruise category and 4 (3.9%) samples with bruising visually rated below 10% were placed into the ≥11% bruise category. The 4 samples misclassified with bruising below 10% were all on the ambiguous change over point of the two defined classification categories at 10% bruising.

These results improved to >95% correctly classified when the fruit were rescanned after 24 hours following impact. It appears the 24 hour time delay allowed more time for the bruising to develop assisting with classification. This would indicate that in a commercial situation it would be an advantage to hold the fruit for 24 hours prior to scanning. The 5 (4.9%) samples misclassified were all samples with bruising visually rated below 10% and placed into the ≥11% bruise category. Of these samples 4 (3.9%) were at the ambiguous

250 (i) 0-30;

250 (i) 0 -10;

or with the geometry of the configurations used.

**3.2 Impact and rot assessment** 

development for the scanned area.

**(n)** 

**Item assessed Spectra** 

**%Rots of scanned area**  NIRS has come to be extensively used in many applications for the non-invasive rapid assessment of a wide variety of products. These both include quantitative compositional determinations and qualitative determinations. The present study indicates the potential of FT-NIRS in diffuse reflectance mode to be used as a non-invasive method to predict the %DM of whole 'Hass' avocado fruit and the importance of incorporating seasonal and geographical variation in the calibration model. The results showed that the calibration model robustness increased when data from more than one season, incorporating a greater range of seasonal variation, was included in the calibration set. Also, that there are spectral differences between geographical regions and that, specific regional models may have significantly reduced predictive performance when applied to samples containing biological variability from a different growing region. It is therefore important that calibrations be developed on populations representative in which sorting is to be attempted.

As shown, there is also great potential to use FT-NIRS as a tool to predict impact damage of whole avocados based on percentage bruise development, and to predict shelf-life based on rot development (susceptibility). It should be considered that the preliminary work presented here is a first step towards shelf-life prediction and bruise detection for avocado fruit. However, this was only a preliminary study and the classification models require many more samples, incorporating seasonal and geographical biological variations, to enable the development of a robust model suitable for commercial use.

Overall, FT-NIR reflectance spectroscopy shows promise for the application in a commercial, in-line setting for the non-destructive evaluation of %DM, bruises and rot susceptibility of whole avocado fruit, although optimisation of the technology is required to address speed of throughput and environmental issues. Incorporating fruit physiological variability over future seasons and growing regions will be essential to further increase model robustness and ensure the predictive performance suitable for commercial use.

Unfortunately, the process of calibration development is a major impediment to the rapid adoption of NIRS in industry. The collection and precise analysis of the reference samples remains a time-consuming and a potentially costly exercise depending on the type of analysis. With this said, NIRS has an obvious place in agriculture and environmental applications with its core strength in the analysis of biological materials, plus low cost of

The Application of Near Infrared Spectroscopy

*Technology* 29(3): 300-307.

Norris." *NIR news* 16(7): 9-11.

*Analytica Chemica Acta* 555(2): 286-291.

*Agricultural Research* 56: 417-426.

*Science* 70(3): 187-191.

*Research* 55(4): 471-476.

265.

Ltd.

Philosophy.

Australia Ltd: 11-12.

*Postharvest Biology and Technology* 11: 1-21.

for the Assessment of Avocado Quality Attributes 227

Clark, C. J., Hockings, P. D., Joyce, D. C. & Mazucco, R. A. (1997). "Application of magnetic

Clark, C. J., McGlone, V. A., Requejo, C., White, A. & Woolf, A. B. (2003). "Dry matter

Cozzolino, D., Esler, M. B., Dambergs, R. G., Cynkar, W. U., Boehm, D. R., Francis, I. L. &

Embry, J. (2009). Avocado retail quality surveys. *4th Australian and New Zealand Avocado Growers Conference 21-24 July 2009.* Cairns, Queensland Australia: 48. Gaete-Garreton, L., Varfas-Hern-Ndez, Y., Leơn-vidal, C. & Pettorino-Besnier, A. (2005). "A

Gamble, J., Wohlers, M. & Jaeger, S. R. (2008). A survey of Australian avocado consumers:

Zealand, The Horticulture and Food Research Institute of New Zealand Ltd. Golic, M. & Walsh, K. B. (2006). "Robustness of calibration models based on near infrared

Guthrie, J., Greensill, C., Bowden, R. & Walsh, K. (2004). "Assessment of quality defects in

Guthrie, J., Wedding, B. & Walsh, K. (1998). "Robustness of NIR calibrations for soluble

Guthrie, J. A. (2005). Robustness of NIR calibrations for assessing fruit quality. *Faculty of* 

Guthrie, J. A., Reid, D. J. & Walsh, K. B. (2005). "Assessment of internal quality attributes of

HAL & AAL (2005). The current and emerging business environment of the industry:

Harker, F. R., Jaeger, S. R., Hofman, P., Bava, C., Thompson, M., Stubbings, B., White, A.,

resonance imaging to pre- and post-harvest studies of fruits and vegetables."

determination in `Hass' avocado by NIR spectroscopy." *Postharvest Biology and* 

Gishen, M. (2004). "Prediction of colour and pH in grapes using a diode array spectrophotometer (400-1100nm)." *Journal of Near Infrared Spectroscopy* 12: 105-111. Davies, T. (2005). "NIR spectroscopy. An introduction to near infrared spectroscopy. Karl H.

novel non-invasive ultrasonic method to assess avocado ripening." *Journal of Food* 

Products experiences, health benefit awareness, and impact of defect on purchase intentions. *Report to HAL and Avocado Australia, Project AV07019*. Auckland, New

spectroscopy for the in-line grading of stonefruit for total soluble solids content."

Macadamia kernels using NIR spectroscopy." *Australian Journal of Agricultural* 

solids in intact melon and pineapple." *Journal of Near Infrared Spectroscopy* 6: 259-

*Arts, Health and Sciences*. Rockhampton, Central Queensland University. Doctor of

mandarin fruit. 2. NIR calibration model robustness." *Australian Journal of* 

overview of the current situation of industry. *2005-2010 Strategic Plan for the Australian Avocado Industry*. Sydney, Australian Avocado Ltd and Horticultural

Wohlers, M., Heffer, M., Lund, C. & Woolf, A. (2007). Australian consumers' perceptions and preferences for 'Hass' avocado. Sydney, Horticulture Australia

analysis, simplicity in sample preparation, no chemical reagent requirements, simultaneous analysis of multiple constituents, good repeatability and high throughput capability.

#### **5. Acknowledgments**

The authors would like to acknowledge the financial support of the Australian Research Council (LP0562294); BRET-TECH; Department of Employment, Economic Development and Innovation (DEEDI); and James Cook University (JCU) for this project.

The authors also wish to thank Lachlan Donovan, Warren Jonsson, Brian Lubach and Aldo Piagno for the supply of fruit; staff from the Horticulture and Forestry Sciences group within the DEEDI for the organising and collection of fruit; Peter Hofman, Jeff Herse, Bonnie Tilse and Jamie Fitzsimmons for technical assistance during the project.

### **6. References**


analysis, simplicity in sample preparation, no chemical reagent requirements, simultaneous

The authors would like to acknowledge the financial support of the Australian Research Council (LP0562294); BRET-TECH; Department of Employment, Economic Development

The authors also wish to thank Lachlan Donovan, Warren Jonsson, Brian Lubach and Aldo Piagno for the supply of fruit; staff from the Horticulture and Forestry Sciences group within the DEEDI for the organising and collection of fruit; Peter Hofman, Jeff Herse, Bonnie

Abbott, J. A. (1999). "Quality measurements of fruit and vegetables." *Postharvest Biology and* 

Avocados Australia Limited (2008). "Avocados Australia New Maturity Standard." *Talking* 

Avocados Australia Limited & Primary Business Solutions (2005). The current and emerging

Baillères, H., Davieux, F. & Ham-Pichavant, F. (2002). "Near infrared analysis as a tool for

Birth, G. S., Dull, G. G., Renfore, W. T. & Kays, S. J. (1985). "Non-destructive

Bobelyn, E., Serban, A.-S., Nicu, M., Lammertyn, J., Nicolai, B. M. & Saeys, W. (2010).

Buning-Pfaue, H. (2003). "Analysis of water in food by near infrared spectroscopy." *Food* 

Butz, P., Hofmann, C. & Tauscher, B. (2005). "Recent developments in noninvasive

Chen, P., McCarthy, M. J., Kauten, R., Sarig, Y. & Han, S. (1993). "Maturity Evaluation of

Chen, P. & Sun, Z. (1991). "A review of non-destructive methods for quality evaluation and

business environment of the industry: overview of the current situation of industry. *2005-2010 strategic plan for the Australian Avocado Industry*. Sydney, Australian

rapid screening of some major wood characteristics in eucalyptus breeding

spectraphotometric determination of dry matter in onions." *Journal of American* 

"Postharvest quality of apple predicted by NIR-spectroscopy: Study of the effect of biological variability on spectra and model performance." *Postharvest Biology and* 

techniques for fresh fruit and vegetable internal quality analysis." *Journal of Food* 

Avocados by NMR Methods." *Journal of Agricultural Engineering Research* 55(3): 177-

sorting of agricultural products." *Journal of Agricultural Engineering Research* 49: 85-

analysis of multiple constituents, good repeatability and high throughput capability.

and Innovation (DEEDI); and James Cook University (JCU) for this project.

Tilse and Jamie Fitzsimmons for technical assistance during the project.

Avocado Ltd and Horticultural Australia Ltd: 11-12.

program." *Annals of Forrest Science* 59: 479-490.

*Society of Horticultural Science* 110: 297-303.

**5. Acknowledgments** 

**6. References** 

*Technology* 15: 207-225.

*Technology* 55(3): 133-143.

*Chemistry* 82: 107–115.

*Science* 70(9): 131-141.

187.

98.

*avocados* 19(4): 24.


The Application of Near Infrared Spectroscopy

*Spectroscopy.* 11(2): 97-107.

*J. For. Res.* 33: 2297-2305.

*1985 Yearbook* 69: 137-144.

PDK projects, Inc.

developments." *The Orchidardist*: 40-45.

Chemist, Inc.

for the Assessment of Avocado Quality Attributes 229

Peirs, A., Tirry, J., Verlinden, B., Darius, P. & Nicolaï, B. M. (2003). "Effect of biological

Peirs, A., Tirry, J., Verlinden, B., Darius, P. & Nicolaï, B. M. (2003). "Effect of biological

Petty, J. & Embry, J. (2011). *Avocado testing helps lead to improved eating quallity for consumers*. VII World Avocado Congress 2011, Cairns Convention Centre, Cairns - Australia. Sánchez, N. H., Lurol, S., Roger, J. M. & Bellon-Maurel, V. (2003). "Robustness of models

Saranwong, S. & Kawano, S. (2007). Fruit and vegetables. In: Near-infrared spectroscopy in

Schimleck, L. R., Mora, C. & Daniels, R. F. (2003). "Estimation of the physical wood

Schmilovitch, Z., Hoffman, A., Egozi, H., El-Batzi, R., Degani, C. & 175-179., p. (2001).

Schroeder, C. A. (1985). "Physiological gradient in avocado fruit." *California Avocado Society* 

Scotter, C. (1990). "Use of near infrared spectroscopy in the food industry with particular reference to its applications to on/in-line food processes." *Food Control*: 142-149. Sivakumar, S. S., Qiao, J., Wang, N., Gariépy, Y. & Raghavan, G. S. V. (2006). *Detecting* 

International Meeting, Oregon Convention Center, Portland, Oregon. Walsh, K. B., Golic, M. & Greensill, C. V. (2004). "Sorting of fruit using near infrared

dry matter content." *Journal of Near Infrared Spectroscopy* 12: 141-148.

Horticulture and Food Research Institue of New Zealand Ltd.

Wedding, B. B., White, R. D., Grauf, S., Wright, C., Tilse, B., Hofman, P. & Gadek, P. A.

Williams, P. (2008). *Near-Infrared Technology - Getting the best out of light* Nanaimo, Canada,

Williams, P. C. & Norris, K. H. (1987). *Qualitative application of near-infrared reflectance* 

Woolf, A., Clark, C., Terander, E., Phetsomphou, V., Hofshi, R., Arpaia, M. L., Boreham, D.,

spectroscopy." *Journal of the Science of Food and Agriculture* 91(2): 233-238. White, A., Woolf, A. & Hofman, P. (2001). *AvoCare Assessment Manual*. New Zealand, The

& Sons, Inc., New Jersey, United States of America.: 219-245.

*Sensors in Horticulture III, Acta Horti. 562 ISHS* 175-179.

*Postharvest Biology and Technology.* 28(2): 269-280.

*Postharvest Biology and Technology* 28: 269-280.

variability on the robustness of NIR models for soluble solids content of apples."

variability on the robustness of NIR models for soluble solids content of apples."

based on NIR spectra for sugar content prediction in apples." *Journal of Near Infrared* 

food science and technology. Y. Ozaki, W. F. McClure & A. A. Christy, John Wiley

properties of green Pinus taeda radial samples by near infrared spectroscopy." *Can.* 

"Determination of avocado maturity by near infrared spectrometry." *Proceedings:* 

*Maturity Parameters of Mango Using Hyperspectral Imaging Technique*. ASABE Annual

spectroscopy: application to a range of fruit and vegetables for soluble solids and

(2010). "Non-destructive prediction of 'Hass' avocado dry matter via FT-NIR

*spectroscopy*. St Paul, Minnesota, USA., The American Association of Cereal

Wong, M. & White, A. (2003). "Measuring avocado maturity; ongoing


Harker, R. (2009). Consumer preferences and choice of fruit: the role of avocado quality. *4th* 

Hartmann, R. & Bijning-Pfaue, H. (1998). "NIR determination of potato constituents." *Potato* 

Hofman, P. & Ledger, S. N. (1999). "Retail surveys showed little quality improvement."

Hofman, P. & Marques, J. (2009). Exporting Australian avocado fruit: technical challenges,

Hruschka, W. R. (1987). *Data analysis: wavelength selection methods*, The American Association

Kim, S., Chen, P., McCarthy, M. & Zion, B. (1999). "Fruit internal quality evaluation using

Lammertyn, J., Peirs, A., De Baerdemaeker, J. & Nicolai, B. (2000). "Light penetration

Liu, Y., Wang, J., Fu, X., Ye, Z. & Lu, H. (2005). *Effect of biological variability on the robustness of* 

Marques, J. R., Hofman, P. J. & Wearing, A. H. (2006). "Between-tree variation in fruit

McGlone, V. A. & Kawano, S. (1998). "Firmness, dry-matter, and soluble-solids assessment

Miyanoto, K. & Yoshinobu, K. (1995). "Non-destructive determination of sugar content in

Mizrach, A. (2000). "Determination of avocado and mango fruit properties." *Ultrasonics* 38:

Mizrach, A. & Flitsanov, U. (1999). "Nondestructive ultrasonic determination of avocado

Nicolaï, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K. I. & Lammertyn, J.

Peiris, K. H. S., Dull, G. G., Leffler, R. G. & Kays, S. J. (1998). "Near-infrared Spectrometric

*Journal of the American Society for Horticultural Science* 123(5): 898-905.

softening process." *Journal of Food Engineering* 40(3): 139-144.

assessment." *Postharvest Biology and Technology* 18(2): 121-132.

Queensland, Australia: 48.

*Talking avocados* 10: 22-23.

*24 July 2009.* Cairns, Queensland, Austrlia.: 46.

of Cereal Chemist, Inc, Minnesota USA.

Tampa Convention Center, Tampa, Florida.

*Experimental Agriculture* 46: 1195-1201.

*Near Infrared Spectroscopy* 3: 227-237.

Longman Group UK Ltd, Harlow, England.

*Research* 41: 327- 334.

*Research* 74: 293-301.

13(2): 131-141.

717-722.

*Australian and New Zealand Avocado Growers Conference 21-24 July 2009.* Cairns,

options and research. *4th Australian and New Zealand Avocado Growers Conference 21-*

online nuclear magnetic resonance sensors." *Journal of Agriculture Engineering* 

properties of NIR radiation in fruit with respect to non-destructive quality

*FT-NIR models for sugar content of pears*. ASAE Annual International Meeting,

quality and fruit mineral concentrations of Hass avocados." *Australian Journal of* 

of postharvest kiwi fruit by NIR spectroscopy." *Postharvest Biology and Technology*

satsuma mandarin fruit by near infrared transmittance spectroscopy." *Journal of* 

(2007). "Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review." *Postharvest Biology and Technology* 46(2): 99-118. Osborne, B. G., Fearn, T. & Hindle, P. H. (1993). *Practical NIR spectroscopy with applications in* 

*food and beverage analysis, second edition.*, Longman Scientific and Technical,

Method for Nondestructive Determination of Soluble Solids Content of Peaches."


**14** 

**Time-Resolved FTIR** 

*VU University of Amsterdam* 

*Netherlands* 

**Difference Spectroscopy** 

Alexandre Maxime and Rienk van Grondelle

**Reveals the Structure and Dynamics** 

**of Carotenoid and Chlorophyll Triplets in** 

**Photosynthetic Light-Harvesting Complexes** 

Infrared spectroscopy is a very powerful tool to determine the chemical nature of

Differential infrared spectroscopy allows to select only those chemical vibrations involved in a light induced reaction. The structure and environment of unstable and short lived excited states can be probed by using step scan FTIR time resolved spectroscopy. In this chapter we present an application of time resolved FTIR step scan spectroscopy to the light harvesting complexes involved in the collection of solar energy in photosynthesis. The time resolved data are analysed using a global and target analysis procedure which allows identification of the dynamic and the spectral properties of short lived intermediates such as triplet states. Triplet state of chlorophyll a (Chl a) can react with oxygen and lead to the formation of singlet oxygen. Carotenoids avoid this reaction via triplet excitation energy transfer (TEET) and quench the triplet of Chl a. The peridinin chlorophyll protein (PCP), an algal light harvesting complex, which binds Per and Chl a is a good system to study photoprotection mechanism by infrared spectroscopy. Indeed Per and Chl a have both conjugated carbonyl groups that are efficient probes of the molecular state in the infrared. We first investigated by step scan spectroscopy the TEET reaction of Per and Chl a in solvent to get their respective spectral signature. Such a study leads to the identification of several mechanisms associated with the formation of triplet states in solution. Secondly the triplet formation is observed in two different PCP complexes leading to the unexpected conclusion that the Per triplet state is delocalised over the Chl a. In a third part we reveal that the same process of triplet sharing between Chl and carotenoid is also present in higher plants, in sharp contrast with purple bacteria for which the triplet is fully localised on the carotenoid. Our finding strongly suggests that in higher plants and algae a much stronger interaction between carotenoids and chlorophylls is at the basis of photoprotection, and represents an example of molecular

**1. Introduction** 

adaptation in oxygenic photosynthesis.

molecules.

Xiaobo, Z., Jiewen, Z. & Yanxiao, L. (2006). "Selection of the efficient wavelength regions in FT-NIR spectroscopy for determination of SSC of 'Fuji' apple based on BiPLS and FiPLS models." *Vibrational Spectroscopy*: 1-8.
