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The use of a model developed on spectra of one (master) instrument with spectra collected using another (slave) instrument requires differences in spectra of master and slave units to be orthogonal to the calibration model. The more spectral similarity is achieved in hardware, i.e. by matching the optical characteristics of the devices, the less chemometric correction is required. The transfer of partial least squares models for total soluble solids (TSS) of intact apple fruit between instrumentation based on silicon photodiode arrays was improved by use of more accurate wavelength assignments over the wavelength range used in the model. Several transfer methodologies were trialled, including piecewise direct standardisation (PDS), transfer by orthogonal projection, model updating (MU) and difference spectrum adjustment. The difference spectrum method combined with new wavelength assignments and MU gave results comparable to the performance of the master instrument and to models directly developed on the slave instruments (r 2 = 0.95, SEP -b = 0.47 and bias = −0.03% TSS, for a population of mean = 14.45 and SD = 1.64% w/v). The use of average difference spectrum adjustment combined with MU was preferred over PDS because of ease of implementation.

The effect of the spectral variation in quartz tungsten lamp output with respect to elapsed time from power-up and variation in environmental temperature, and the variation in readout in the front-end electronics (FEE) and spectrometer with temperature, on predictive model performance of total soluble solids (TSS) in intact fruit was assessed for a silicon photodiode spectrometer-based system. Lamp (10 each of OSRAM HLX64623 and Sylvania 521995 12 V 100 W GY6.35 quartz tungsten halogen) output was assessed at 10 s intervals over a 4 h period, and 10 min intervals over approximately 3000 h. The environmental temperature of each component in a near infrared (NIR) spectroscopy system (lamp, FEE, spectrometer) was incrementally adjusted in 10°C intervals between 10°C and 60°C. The lamp output was spectrally stable within the time of the first measurement (10 s), although total illumination was not stable until approximately 40 min from start-up. Thus, the performance of the predictive models based on second derivative of absorbance data was not significantly impacted by lamp warm-up time. Noise on measurement associated with the use of a single white reference resulted in a mean increase in root mean square error of prediction (RMSEP) as high as 0.22% TSS and individual increases as high as 0.82%. Averages of white reference measurements significantly improved performance. When predictive models were developed using second derivative absorbance data and averaged (10) white references, there was no statistically significant impact in RMSEPs on time of lamp warm-up (after 10 s), even during the last hours of lamp life. Spectral variation resulting from changes of NIR system components (lamp and FEE) also affected lamp output quantity rather than quality and thus did not affect the predictive performance owing to the second derivative absorbance pretreatment. Some lamps displayed start-up output characteristics on their first use that were not repeated in subsequent trials. This result indicates the need for a short lamp "burn-in" period.

Understanding of light-emitting diode lamp behaviour is essential to support the use of these devices as illumination sources in near infrared spectroscopy. Spectral variation in light-emitting diode peak output (680, 700, 720, 735, 760, 780, 850, 880 and 940 nm) was assessed over time from power up and with variation in environmental temperature. Initial light-emitting diode power up to full intensity occurred within a measurement cycle (12 ms), then intensity decreased exponentially over approximately 6 min, a result ascribed to an increase in junction temperature as current is passed through the light-emitting diode. Some light-emitting diodes displayed start-up output characteristics on their first use, indicating the need for a short light-emitting diode ‘burn in’ period, which was less than 24 h in all cases. Increasing the ambient temperature produced a logarithmic decrease in overall intensity of the light-emitting diodes and a linear shift to longer wavelength of the peak emission. This behaviour is consistent with the observed decrease in the IAD Index (absorbance difference between 670 nm and 720 nm, A670–A720) with increased ambient temperature, as measured by an instrument utilising light-emitting diode illumination (DA Meter). Instruments using light-emitting diodes should be designed to avoid or accommodate the effect of temperature. If accommodating temperature, as light-emitting diode manufacturer specifications are broad, characterisation is recommended.

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Globally, universities are striving to increase enrolment rates, especially for low socioeconomic status and mature-aged students. In order to meet these targets, universities are accepting a broader range of students, often resulting in a widening mathematical knowledge gap between secondary school and university (Hoyles, Newman & Noss, 2001). Therefore, even amid the growing trend of scaling back services, there exists a need for extra learning support in mathematics. Mathematics support services are recognised as vital in assisting students to both bridge the knowledge gap and become independent learners. Through a survey of students using the Mathematics Learning Centre at Central Queensland University Australia, it was found that the implementation of scaffolding, adult learning principles and the embedding of mathematics support provides students with not only fundamental mathematical knowledge but also the skills required to become self-directed learners.

Handheld diode array based SWNIR instruments are being used in field assessment of attributes of tree fruit. Sampling statistics dictate that for a population with a (typical) standard deviation of 1.5 % fruit total soluble solids (TSS) at 95% confidence interval with a margin of error of 0.2%, at least 225 fruit should be sampled, an application suited to a rapid non-destructive method such as NIRS. Unfortunately, field users tend to place less emphasis on instrument maintenance, so understanding of performance issues is important. The performance over time of a visible-shortwave near infrared spectrophotometer used in estimation of fruit attributes will depend on several factors, including aging of the light source, and ambient temperature of lamp and detector system. 1 Changes in the detector can include changes in relative spectral sensitivity, wavelength drift and degradation of detector signal to noise ratio. For example, Greensill et al. 2 demonstrated that for the application of assessment of sucrose concentration of aqueous solutions on cellulose fibre, model performance was decreased if wavelength resolution decreased beyond approximately 10 nm (FWHM) and repeatability decreased below approx. 0.1 mA (assessed as SD of repeated measures of a white reference, relative to that reference). Change in either detector or lamp response will impact the output of a predictive model of fruit attributes, primarily in terms of bias. 3, 4 However, while change in ambient temperature is known to affect halogen lamp output, but in practice spectral quality is not affected sufficiently to impact TSS model predictions, and ageing of a halogen lamp is also not associated with changes in light quality, at least until near lamp failure. 3, 5 Increasing temperature also affects silicon photodiode photo-response (becoming more sensitive to longer wavelengths), and also increases thermal noise. Other instrument changes may also occur over time, affecting performance, e.g. probe alignment and wave

Project based learning is becoming widely accepted within engineering programmes across Australia. Supporters of this method of learning and teaching claim the method immerses the student in the subject and allows them to learn through hands on activities. Students are assessed based on their ability to complete a given project within a team environment. We examine the project based learning engineering programme at Central Queensland University and the students' opinions of its components. Present research and practice into authentic assessment and project based learning are investigated and comparison is made between these and the responses of the students.